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REFERENCES CITED

Abraham, B., & Ledolter, J. (1983). Statistical methods for forecasting. New York: Wiley.

Adorno, T. W., Frenkel-Brunswik, E., Levinson, D. J., & Sanford, R. N. (1950). The authoritarian personality. New York: Harper.

Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD Conference, Washington, DC.

Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th VLDB Conference. Santiago, Chile.

Agresti, Alan (1996). An Introduction to Categorical Data Analysis. New York: Wiley.

Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov and F. Csaki (Eds.), Second International Symposium on Information Theory. Budapest: Akademiai Kiado.

Akaike, H. (1983). Information measures and model selection. Bulletin of the International Statistical Institute: Proceedings of the 44th Session, Volume 1. Pages 277-290.

Aldrich, J. H., & Nelson, F. D. (1984). Linear probability, logit, and probit models. Beverly Hills, CA: Sage Publications.

Almon, S. (1965). The distributed lag between capital appropriations and expenditures. Econometrica, 33, 178-196.

American Supplier Institute (1984-1988). Proceedings of Supplier Symposia on Taguchi Methods. (April, 1984; November, 1984; October, 1985; October, 1986; October, 1987; October, 1988), Dearborn, MI: American Supplier Institute.

Anderson, O. D. (1976). Time series analysis and forecasting. London: Butterworths.

Anderson, S. B., & Maier, M. H. (1963). 34,000 pupils and how they grew. Journal of Teacher Education, 14, 212-216.

Anderson, T. W. (1958). An introduction to multivariate statistical analysis. New York: Wiley.

Anderson, T. W. (1984). An introduction to multivariate statistical analysis (2nd ed.). New York: Wiley.

Anderson, T. W., & Rubin, H. (1956). Statistical inference in factor analysis. Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: The University of California Press.

Andrews, D. F. (1972). Plots of high-dimensional data. Biometrics, 28, 125-136.

ASQC/AIAG (1990). Measurement systems analysis reference manual. Troy, MI: AIAG.

ASQC/AIAG (1991). Fundamental statistical process control reference manual. Troy, MI: AIAG.

AT&T (1956). Statistical quality control handbook, Select code 700-444. Indianapolis, AT&T Technologies.

Auble, D. (1953). Extended tables for the Mann-Whitney statistic. Bulletin of the Institute of Educational Research, Indiana University, 1, No. 2.

Bagozzi, R. P., & Yi, Y. (1989). On the use of structural equation models in experimental design. Journal of Marketing Research, 26, 271-284.

Bagozzi, R. P., Yi, Y., & Singh, S. (1991). On the use of structural equation models in experimental designs: Two extensions. International Journal of Research in Marketing, 8, 125-140.

Bailey, A. L. (1931). The analysis of covariance. Journal of the American Statistical Association, 26, 424-435.

Bails, D. G., & Peppers, L. C. (1982). Business fluctuations: Forecasting techniques and applications. Englewood Cliffs, NJ: Prentice-Hall.

Bain, L. J. (1978). Statistical analysis of reliability and life-testing models. New York: Decker.

Bain, L. J. and Engelhardt, M. (1989) Introduction to Probability and Mathematical Statistics. Kent, MA: PWS.

Baird, J. C. (1970). Psychophysical analysis of visual space. New York: Pergamon Press.

Baird, J. C., & Noma, E. (1978). Fundamentals of scaling and psychophysics. New York: Wiley.

Barcikowski, R., & Stevens, J. P. (1975). A Monte Carlo study of the stability of canonical correlations, canonical weights, and canonical variate-variable correlations. Multivariate Behavioral Research, 10, 353-364.

Barker, T. B. (1986). Quality engineering by design: Taguchi's philosophy. Quality Progress, 19, 32-42.

Barlow, R. E., & Proschan, F. (1975). Statistical theory of reliability and life testing. New York: Holt, Rinehart, & Winston.

Barlow, R. E., Marshall, A. W., & Proschan, F. (1963). Properties of probability distributions with monotone hazard rate. Annals of Mathematical Statistics, 34, 375-389.

Barnard, G. A. (1959). Control charts and stochastic processes. Journal of the Royal Statistical Society, Ser. B, 21, 239.

Bartholomew, D. J. (1984). The foundations of factor analysis. Biometrika, 71, 221-232.

Bates, D. M., & Watts, D. G. (1988). Nonlinear regression analysis and its applications. New York: Wiley.

Bayne, C. K., & Rubin, I. B. (1986). Practical experimental designs and optimization methods for chemists. Deerfield Beach, FL: VCH Publishers.

Becker, R. A., Denby, L., McGill, R., & Wilks, A. R. (1986). Datacryptanalysis: A case study. Proceedings of the Section on Statistical Graphics, American Statistical Association, 92-97.

Bellman, R. (1961). Adaptive Control Processes: A Guided Tour. Princeton University Press.

Belsley, D. A., Kuh, E., and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

Bendat, J. S. (1990). Nonlinear system analysis and identification from random data. New York: Wiley.

Bentler, P. M, & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588-606.

Bentler, P. M. (1986). Structural modeling and Psychometrika: A historical perspective on growth and achievements. Psychometrika, 51, 35-51.

Bentler, P. M. (1989). EQS Structural equations program manual. Los Angeles, CA: BMDP Statistical Software.

Bentler, P. M., & Weeks, D. G. (1979). Interrelations among models for the analysis of moment structures. Multivariate Behavioral Research, 14, 169-185.

Bentler, P. M., & Weeks, D. G. (1980). Linear structural equations with latent variables. Psychometrika, 45, 289-308.

Benzécri, J. P. (1973). L'Analyse des Données: T. 2, I' Analyse des correspondances. Paris: Dunod.

Bergeron, B. (2002). Essentials of CRM: A guide to customer relationship management. NY: Wiley.

Berkson, J. (1944). Application of the Logistic Function to Bio-Assay. Journal of the American Statistical Association, 39, 357-365.

Berkson, J., & Gage, R. R. (1950). The calculation of survival rates for cancer. Proceedings of Staff Meetings, Mayo Clinic, 25, 250.

Berry, M., J., A., & Linoff, G., S., (2000). Mastering data mining. New York: Wiley.

Bhote, K. R. (1988). World class quality. New York: AMA Membership Publications.

Binns, B., & Clark, N. (1986). The graphic designer's use of visual syntax. Proceedings of the Section on Statistical Graphics, American Statistical Association, 36-41.

Birnbaum, Z. W. (1952). Numerical tabulation of the distribution of Kolmogorov's statistic for finite sample values. Journal of the American Statistical Association, 47, 425-441.

Birnbaum, Z. W. (1953). Distribution-free tests of fit for continuous distribution functions. Annals of Mathematical Statistics, 24, 1-8.

Bishop, C. (1995). Neural Networks for Pattern Recognition. Oxford: University Press.

Bishop, Y. M. M., Fienberg, S. E., & Holland, P. W. (1975). Discrete multivariate analysis. Cambridge, MA: MIT Press.

Bjorck, A. (1967). Solving linear least squares problems by Gram-Schmidt orthonormalization. Bit, 7, 1-21.

Blackman, R. B., & Tukey, J. (1958). The measurement of power spectral from the point of view of communication engineering. New York: Dover.

Blackwelder, R. A. (1966). Taxonomy: A text and reference book. New York: Wiley.

Blalock, H. M. (1972). Social statistics (2nd ed.). New York:McGraw-Hill

Bliss, C. I. (1934). The method of probits. Science, 79, 38-39.

Bloomfield, P. (1976). Fourier analysis of time series: An introduction. New York: Wiley.

Bock, R. D. (1963). Programming univariate and multivariate analysis of variance. Technometrics, 5, 95-117.

Bock, R. D. (1975). Multivariate statistical methods in behavioral research. New York: McGraw-Hill.

Bolch, B.W., & Huang, C. J. (1974). Multivariate statistical methods for business and economics. Englewood Cliffs, NJ: Prentice-Hall.

Bollen, K. A. (1989). Structural equations with latent variables. New York: John Wiley & Sons.

Borg, I., & Lingoes, J. (1987). Multidimensional similarity structure analysis. New York: Springer.

Borg, I., & Shye, S. (in press). Facet Theory. Newbury Park: Sage.

Bouland, H. and Kamp, Y. (1988). Auto-association by multilayer perceptrons and singular value decomposition. Biological Cybernetics 59, 291-294.

Bowker, A. G. (1948). A test for symmetry in contingency tables. Journal of the American Statistical Association, 43, 572-574.

Bowley, A. L. (1897). Relations between the accuracy of an average and that of its constituent parts. Journal of the Royal Statistical Society, 60, 855-866.

Bowley, A. L. (1907). Elements of Statistics. London: P. S. King and Son.

Box, G. E. P. (1953). Non-normality and tests on variances. Biometrika, 40, 318-335.

Box, G. E. P. (1954a). Some theorems on quadratic forms applied in the study of analysis of variance problems: I. Effect of inequality of variances in the one-way classification. Annals of Mathematical Statistics, 25, 290-302.

Box, G. E. P. (1954b). Some theorems on quadratic forms applied in the study of analysis of variance problems: II. Effect of inequality of variances and of correlation of errors in the two-way classification. Annals of Mathematical Statistics, 25, 484-498.

Box, G. E. P., & Anderson, S. L. (1955). Permutation theory in the derivation of robust criteria and the study of departures from assumptions. Journal of the Royal Statistical Society, 17, 1-34.

Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2, 455-475.

Box, G. E. P., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society, 26, 211-253.

Box, G. E. P., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society, B26, 211-234.

Box, G. E. P., & Draper, N. R. (1987). Empirical model-building and response surfaces. New York: Wiley.

Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis. San Francisco: Holden Day.

Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control. San Francisco: Holden-Day.

Box, G. E. P., & Tidwell, P. W. (1962). Transformation of the independent variables. Technometrics, 4, 531-550.

Box, G. E. P., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Ser. B, 13, 1-45.

Box, G. E. P., Hunter, W. G., & Hunter, S. J. (1978). Statistics for experimenters: An introduction to design, data analysis, and model building. New York: Wiley.

Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software.

Brenner, J. L., et al. (1968). Difference equations in forecasting formulas. Management Science, 14, 141-159.

Brent, R. F. (1973). Algorithms for minimization without derivatives. Englewood Cliffs, NJ: Prentice-Hall.

Breslow, N. E. (1970). A generalized Kruskal-Wallis test for comparing K samples subject to unequal pattern of censorship. Biometrika, 57, 579-594.

Breslow, N. E. (1974). Covariance analysis of censored survival data. Biometrics, 30, 89-99.

Bridle, J.S. (1990). Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. In F. Fogelman Soulie and J. Herault (Eds.), Neurocomputing: Algorithms, Architectures and Applications, 227-236. New York: Springer-Verlag.

Brigham, E. O. (1974). The fast Fourier transform. Englewood Cliffs, NJ: Prentice-Hall.

Brillinger, D. R. (1975). Time series: Data analysis and theory. New York: Holt, Rinehart. & Winston.

Broomhead, D.S. and Lowe, D. (1988). Multivariable functional interpolation and adaptive networks. Complex Systems 2, 321-355.

Brown, D. T. (1959). A note on approximations to discrete probability distributions. Information and Control, 2, 386-392.

Brown, M. B., & Forsythe, A. B. (1974). Robust tests for the equality of variances. Journal of the American Statistical Association, 69, 264-267.

Brown, R. G. (1959). Statistical forecasting for inventory control. New York: McGraw-Hill.

Browne, M. W. (1968). A comparison of factor analytic techniques. Psychometrika, 33, 267-334.

Browne, M. W. (1974). Generalized least squares estimators in the analysis of covariance structures. South African Statistical Journal, 8, 1-24.

Browne, M. W. (1982). Covariance Structures. In D. M. Hawkins (Ed.) Topics in Applied Multivariate Analysis. Cambridge, MA: Cambridge University Press.

Browne, M. W. (1984). Asymptotically distribution free methods for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 37, 62-83.

Browne, M. W., & Cudeck, R. (1990). Single sample cross-validation indices for covariance structures. Multivariate Behavioral Research, 24, 445-455.

Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. In K. A. Bollen and J. S. Long (Eds.), Testing structural equation models. Beverly Hills, CA: Sage.

Browne, M. W., & DuToit, S. H. C. (1982). AUFIT (Version 1). A computer programme for the automated fitting of nonstandard models for means and covariances. Research Finding WS-27. Pretoria, South Africa: Human Sciences Research Council.

Browne, M. W., & DuToit, S. H. C. (1987). Automated fitting of nonstandard models. Report WS-39. Pretoria, South Africa: Human Sciences Research Council.

Browne, M. W., & DuToit, S. H. C. (1992). Automated fitting of nonstandard models. Multivariate Behavioral Research, 27, 269-300.

Browne, M. W., & Mels, G. (1992). RAMONA User's Guide. The Ohio State University: Department of Psychology.

Browne, M. W., & Shapiro, A. (1989). Invariance of covariance structures under groups of transformations. Research Report 89/4. Pretoria, South Africa: University of South Africa Department of Statistics.

Browne, M. W., & Shapiro, A. (1991). Invariance of covariance structures under groups of transformations. Metrika, 38, 335-345.

Browne, M.W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long, (Eds.), Testing structural equation models. Beverly Hills, CA: Sage.

Brownlee, K. A. (1960). Statistical Theory and Methodology in Science and Engineering. New York: John Wiley.

Buffa, E. S. (1972). Operations management: Problems and models (3rd. ed.). New York: Wiley.

Buja, A., & Tukey, P. A. (Eds.) (1991). Computing and Graphics in Statistics. New York: Springer-Verlag.

Buja, A., Fowlkes, E. B., Keramidas, E. M., Kettenring, J. R., Lee, J. C., Swayne, D. F., & Tukey, P. A. (1986). Discovering features of multivariate data through statistical graphics. Proceedings of the Section on Statistical Graphics, American Statistical Association, 98-103.

Burman, J. P. (1979). Seasonal adjustment - a survey. Forecasting, Studies in Management Science, 12, 45-57.

Burns, L. S., & Harman, A. J. (1966). The complex metropolis, Part V of profile of the Los Angeles metropolis: Its people and its homes. Los Angeles: University of Chicago Press.

Burt, C. (1950). The factorial analysis of qualitative data. British Journal of Psychology, 3, 166-185.

Campbell D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81-105

Carling, A. (1992). Introducing Neural Networks. Wilmslow, UK: Sigma Press.

Carmines, E. G., & Zeller, R. A. (1980). Reliability and validity assessment. Beverly Hills, CA: Sage Publications.

Carrol, J. D., Green, P. E., and Schaffer, C. M. (1986). Interpoint distance comparisons in correspondence analysis. Journal of Marketing Research, 23, 271-280.

Carroll, J. D., & Wish, M. (1974). Multidimensional perceptual models and measurement methods. In E. C. Carterette and M. P. Friedman (Eds.), Handbook of perception. (Vol. 2, pp. 391-447). New York: Academic Press.

Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1, 245-276.

Cattell, R. B., & Jaspers, J. A. (1967). A general plasmode for factor analytic exercises and research. Multivariate Behavioral Research Monographs.

Chambers, J. M., Cleveland, W. S., Kleiner, B., & Tukey, P. A. (1983). Graphical methods for data analysis. Bellmont, CA: Wadsworth.

Chan, L. K., Cheng, S. W., & Spiring, F. (1988). A new measure of process capability: Cpm. Journal of Quality Technology, 20, 162-175.

Chen, J. (1992). Some results on 2(nk) fractional factorial designs and search for minimum aberration designs. Annals of Statistics, 20, 2124-2141.

Chen, J., & Wu, C. F. J. (1991). Some results on s(nk) fractional factorial designs with minimum aberration or optimal moments. Annals of Statistics, 19, 1028-1041.

Chen, J., Sun, D. X., & Wu, C. F. J. (1993). A catalog of two-level and three-level fractional factorial designs with small runs. International Statistical Review, 61, 131-145.

Chernoff, H. (1973). The use of faces to represent points in k-dimensional space graphically. Journal of American Statistical Association, 68, 361-368.

Christ, C. (1966). Econometric models and methods. New York: Wiley.

Clarke, G. M., & Cooke, D. (1978). A basic course in statistics. London: Edward Arnold.

Clements, J. A. (1989). Process capability calculations for non-normal distributions. Quality Progress. September, 95-100.

Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74, 829-836.

Cleveland, W. S. (1984). Graphs in scientific publications. The American Statistician, 38, 270-280.

Cleveland, W. S. (1985). The elements of graphing data. Monterey, CA: Wadsworth.

Cleveland, W. S. (1993). Visualizing data. Murray Hill, NJ: AT&T.

Cleveland, W. S., Harris, C. S., & McGill, R. (1982). Judgements of circle sizes on statistical maps. Journal of the American Statistical Association, 77, 541-547.

Cliff, N. (1983). Some cautions concerning the application of causal modeling methods. Multivariate Behavioral Research, 18, 115-126.

Cochran, W. G. (1950). The comparison of percentages in matched samples. Biometrika, 37, 256-266.

Cohen, J. (1977). Statistical power analysis for the behavioral sciences. (Rev. ed.). New York: Academic Press.

Cohen, J. (1983). Statistical power analysis for the behavioral sciences. (2nd Ed.). Mahwah, NJ: Lawrence Erlbaum Associates.

Cohen, J. (1994). The earth is round (p < .05). American Psychologist, 49, 9971003.

Cole, D. A., Maxwell, S. E., Arvey, R., & Salas, E. (1993). Multivariate group comparisons of variable systems: MANOVA and structural equation modeling. Psychological Bulletin, 114, 174-184.

Connor, W. S., & Young, S. (1984). Fractional factorial experiment designs for experiments with factors at two and three levels. In R. A. McLean & V. L. Anderson (Eds.), Applied factorial and fractional designs. New York: Marcel Dekker.

Connor, W. S., & Zelen, M. (1984). Fractional factorial experiment designs for factors at three levels. In R. A. McLean & V. L. Anderson (Eds.), Applied factorial and fractional designs. New York: Marcel Dekker.

Conover, W. J. (1974). Some reasons for not using the Yates continuity correction on 2 x 2 contingency tables. Journal of the American Statistical Association, 69, 374-376.

Conover, W. J., Johnson, M. E., & Johnson, M. M. (1981). A comparative study of tests for homogeneity of variances with applications to the outer continental shelf bidding data. Technometrics, 23, 357-361.

Cook, R. D. (1977). Detection of influential observations in linear regression. Technometrics, 19, 15-18.

Cook, R. D., & Nachtsheim, C. J. (1980). A comparison of algorithms for constructing exact D-optimal designs. Technometrics, 22, 315-324.

Cook, R. D., & Weisberg, S. (1982). Residuals and Influence in Regression. (Monographs on statistics and applied probability). New York: Chapman and Hall.

Cooke, D., Craven, A. H., & Clarke, G. M. (1982). Basic statistical computing. London: Edward Arnold.

Cooley, J. W., & Tukey, J. W. (1965). An algorithm for the machine computation of complex Fourier series. Mathematics of Computation, 19, 297-301.

Cooley, W. W., & Lohnes, P. R. (1971). Multivariate data analysis. New York: Wiley.

Cooley, W. W., & Lohnes, P. R. (1976). Evaluation research in education. New York: Wiley.

Coombs, C. H. (1950). Psychological scaling without a unit of measurement. Psychological Review, 57, 145-158.

Coombs, C. H. (1964). A theory of data. New York: Wiley.

Corballis, M. C., & Traub, R. E. (1970). Longitudinal factor analysis. Psychometrika, 35, 79-98.

Corbeil, R. R., & Searle, S. R. (1976). Restricted maximum likelihood (REML) estimation of variance components in the mixed model. Technometrics, 18, 31-38.

Cormack, R. M. (1971). A review of classification. Journal of the Royal Statistical Society, 134, 321-367.

Cornell, J. A. (1990a). How to run mixture experiments for product quality. Milwaukee, Wisconsin: ASQC.

Cornell, J. A. (1990b). Experiments with mixtures: designs, models, and the analysis of mixture data (2nd ed.). New York: Wiley.

Cox, D. R. (1957). Note on grouping. Journal of the American Statistical Association, 52, 543-547.

Cox, D. R. (1959). The analysis of exponentially distributed life-times with two types of failures. Journal of the Royal Statistical Society, 21, 411-421.

Cox, D. R. (1964). Some applications of exponential ordered scores. Journal of the Royal Statistical Society, 26, 103-110.

Cox, D. R. (1970). The analysis of binary data. New York: Halsted Press.

Cox, D. R. (1972). Regression models and life tables. Journal of the Royal Statistical Society, 34, 187-220.

Cox, D. R., & Oakes, D. (1984). Analysis of survival data. New York: Chapman & Hall.

Cramer, H. (1946). Mathematical methods in statistics. Princeton, NJ: Princeton University Press.

Cristianini, N., & Shawe-Taylor, J. (2000). Introduction to support vector machines and other kernel-based learning methods. Cambridge, UK: Cambridge University Press.

Crowley, J., & Hu, M. (1977). Covariance analysis of heart transplant survival data. Journal of the American Statistical Association, 72, 27-36.

Cudeck, R. (1989). Analysis of correlation matrices using covariance structure models. Psychological Bulletin, 105, 317-327.

Cudeck, R., & Browne, M. W. (1983). Cross-validation of covariance structures. Multivariate Behavioral Research, 18, 147-167.

Cutler, S. J., & Ederer, F. (1958). Maximum utilization of the life table method in analyzing survival. Journal of Chronic Diseases, 8, 699-712.

Dahlquist, G., & Bjorck, A. (1974). Numerical Methods. Englewood Cliffs, NJ: Prentice-Hall.

Daniel, C. (1976). Applications of statistics to industrial experimentation. New York: Wiley.

Daniell, P. J. (1946). Discussion on symposium on autocorrelation in time series. Journal of the Royal Statistical Society, Suppl. 8, 88-90.

Daniels, H. E. (1939). The estimation of components of variance. Supplement to the Journal of the Royal Statistical Society, 6, 186-197.

Darlington, R. B. (1990). Regression and linear models. New York: McGraw-Hill.

Darlington, R. B., Weinberg, S., & Walberg, H. (1973). Canonical variate analysis and related techniques. Review of Educational Research, 43, 433-454.

DataMyte (1992). DataMyte handbook. Minnetonka, MN.

David, H. A. (1995). First (?) occurrence of common terms in mathematical statistics. The American Statistician, 49, 121-133.

Davies, P. M., & Coxon, A. P. M. (1982). Key texts in multidimensional scaling. Exeter, NH: Heinemann Educational Books.

Davis, C. S., & Stephens, M. A. Approximate percentage points using Pearson curves. Applied Statistics, 32, 322-327.

De Boor, C. (1978). A practical guide to splines. New York: Springer-Verlag.

DeCarlo, L. T. (1998). Signal detection theory and generalized linear models, Psychological Methods, 186-200.

De Gruijter, P. N. M., & Van Der Kamp, L. J. T. (Eds.). (1976). Advances in psychological and educational measurement. New York: Wiley.

de Jong, S (1993) SIMPLS: An Alternative Approach to Partial Least Squares Regression, Chemometrics and Intelligent Laboratory Systems, 18, 251-263

de Jong, S and Kiers, H. (1992) Principal Covariates regression, Chemometrics and Intelligent Laboratory Systems, 14, 155-164

Deming, S. N., & Morgan, S. L. (1993). Experimental design: A chemometric approach. (2nd ed.). Amsterdam, The Netherlands: Elsevier Science Publishers B.V.

Deming, W. E., & Stephan, F. F. (1940). The sampling procedure of the 1940 population census. Journal of the American Statistical Association, 35, 615-630.

Dempster, A. P. (1969). Elements of Continuous Multivariate Analysis. San Francisco: Addison-Wesley.

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39, 1-38.

Dennis, J. E., & Schnabel, R. B. (1983). Numerical methods for unconstrained optimization and nonlinear equations. Englewood Cliffs, NJ: Prentice Hall.

Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12, 214-219.

Diamond, W. J. (1981). Practical experimental design. Belmont, CA: Wadsworth.

Dijkstra, T. K. (1990). Some properties of estimated scale invariant covariance structures. Psychometrika, 55, 327-336.

Dinneen, L. C., & Blakesley, B. C. (1973). A generator for the sampling distribution of the Mann Whitney U statistic. Applied Statistics, 22, 269-273.

Dixon, W. J. (1954). Power under normality of several non-parametric tests. Annals of Mathematical Statistics, 25, 610-614.

Dixon, W. J., & Massey, F. J. (1983). Introduction to statistical analysis (4th ed.). New York: McGraw-Hill.

Dobson, A. J. (1990). An introduction to generalized linear models. New York: Chapman & Hall.

Dodd, B. (1979). Lip reading in infants: Attention to speech presented in- and out-of-synchrony. Cognitive Psychology, 11, 478-484.

Dodge, Y. (1985). Analysis of experiments with missing data. New York: Wiley.

Dodge, Y., Fedorov, V. V., & Wynn, H. P. (1988). Optimal design and analysis of experiments. New York: North-Holland.

Dodson, B. (1994). Weibull analysis. Milwaukee, Wisconsin: ASQC.

Doyle, P. (1973). The use of Automatic Interaction Detection and similar search procedures. Operational Research Quarterly, 24, 465-467.

Duncan, A. J. (1974). Quality control and industrial statistics. Homewood, IL: Richard D. Irwin.

Duncan, A. J. (1986). Quality Control and industrial statistics. 5th ed. Chicago: Irwin.

Duncan, O. D., Haller, A. O., & Portes, A. (1968). Peer influence on aspiration: a reinterpretation. American Journal of Sociology, 74, 119-137.

Dunnett, C. W. (1955). A multiple comparison procedure for comparing several treatments with a control. Journal of the American Statistical Association, 50, 1096-1121.

Durbin, J. (1970). Testing for serial correlation in least-squares regression when some of the regressors are lagged dependent variables. Econometrica, 38, 410-421.

Durbin, J., & Watson, G. S. (1951). Testing for serial correlations in least squares regression. II. Biometrika, 38, 159-178.

Dykstra, O. Jr. (1971). The augmentation of experimental data to maximize |X'X|. Technometrics, 13, 682-688.

Eason, E. D., & Fenton, R. G. (1974). A comparison of numerical optimization methods for engineering design. ASME Paper 73-DET-17.

Edelstein, H., A. (1999). Introduction to data mining and knowledge discovery (3rd ed). Potomac, MD: Two Crows Corp.

Edgeworth, F. Y. (1885). Methods of statistics. In Jubilee Volume, Royal Statistical Society, 181-217.

Efron, B. (1982). The jackknife, the bootstrap, and other resampling plans. Philadelphia, Pa. Society for Industrial and Applied Mathematics.

Eisenhart, C. (1947). The assumptions underlying the analysis of variance. Biometrics, 3, 1-21.

Elandt-Johnson, R. C., & Johnson, N. L. (1980). Survival models and data analysis. New York: Wiley.

Elliott, D. F., & Rao, K. R. (1982). Fast transforms: Algorithms, analyses, applications. New York: Academic Press.

Elsner, J. B., Lehmiller, G. S., & Kimberlain, T. B. (1996). Objective classification of Atlantic hurricanes. Journal of Climate, 9, 2880-2889.

Enslein, K., Ralston, A., & Wilf, H. S. (1977). Statistical methods for digital computers. New York: Wiley.

Euler, L. (1782). Recherches sur une nouvelle espece de quarres magiques. Verhandelingen uitgegeven door het zeeuwsch Genootschap der Wetenschappen te Vlissingen, 9, 85-239. (Reproduced in Leonhardi Euleri Opera Omnia. Sub auspiciis societatis scientiarium naturalium helveticae, 1st series, 7, 291-392.)

Evans, M., Hastings, N., & Peacock, B. (1993). Statistical Distributions. New York: Wiley.

Everitt, B. S. (1977). The analysis of contingency tables. London: Chapman & Hall.

Everitt, B. S. (1984). An introduction to latent variable models. London: Chapman and Hall.

Ewan, W. D. (1963). When and how to use Cu-sum charts. Technometrics, 5, 1-32.

Fahlman, S.E. (1988). Faster-learning variations on back-propagation: an empirical study. In D. Touretzky, G.E. Hinton and T.J. Sejnowski (Eds.), Proceedings of the 1988 Connectionist Models Summer School, 38-51. San Mateo, CA: Morgan Kaufmann.

Fausett, L. (1994). Fundamentals of Neural Networks. New York: Prentice Hall.

Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (Eds.). 1996. Advances in Knowledge Discovery and Data Mining. Cambridge, MA: The MIT Press.

Fayyad, U. S., & Uthurusamy, R. (Eds.) (1994). Knowledge Discovery in Databases; Papers from the 1994 AAAI Workshop. Menlo Park, CA: AAAI Press.

Feigl, P., & Zelen, M. (1965). Estimation of exponential survival probabilities with concomitant information. Biometrics, 21, 826-838.

Feller, W. (1948). On the Kolmogorov-Smirnov limit theorems for empirical distributions. Annals of Mathematical Statistics, 19, 177-189.

Fetter, R. B. (1967). The quality control system. Homewood, IL: Richard D. Irwin.

Fienberg, S. E. (1977). The analysis of cross-classified categorical data. Cambridge, MA: MIT Press.

Finn, J. D. (1974). A general model for multivariate analysis. New York: Holt, Rinehart & Winston.

Finn, J. D. (1977). Multivariate analysis of variance and covariance. In K. Enslein, A. Ralston, and H. S. Wilf (Eds.), Statistical methods for digital computers. Vol. III. (pp. 203-264). New York: Wiley.

Finney, D. J. (1944). The application of probit analysis to the results of mental tests. Psychometrika, 9, 31-39.

Finney, D. J. (1971). Probit analysis. Cambridge, MA: Cambridge University Press.

Firmin, R. (2002). Advanced time series modeling for semiconductor process control: The fab as a time machine. In Mackulak, G. T., Fowler, J. W., & Schomig, A. (eds.). Proceedings of the International Conference on Modeling and Analysis of Semiconductor Manufacturing (MASM 2002).

Fisher, R. A. (1918). The correlation between relatives on the supposition of Mendelian inheritance. Transactions of the Royal Society of Edinburgh, 52, 399-433.

Fisher, R. A. (1922). On the interpretation of Chi-square from contingency tables, and the calculation of p. Journal of the Royal Statistical Society, 85, 87-94.

Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London, Ser. A, 222, 309-368.

Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33, 503-513.

Fisher, R. A. (1928). The general sampling distribution of the multiple correlation coefficient. Proceedings of the Royal Society of London, Ser. A, 121, 654-673.

Fisher, R. A. (1935). The Design of Experiments. Edinburgh: Oliver and Boyd.

Fisher, R. A. (1936). Statistical Methods for Research Workers (6th ed.). Edinburgh: Oliver and Boyd.

Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, 179-188.

Fisher, R. A. (1938). The mathematics of experimentation. Nature, 142, 442-443.

Fisher, R. A., & Yates, F. (1934). The 6 x 6 Latin squares. Proceedings of the Cambridge Philosophical Society, 30, 492-507.

Fisher, R. A., & Yates, F. (1938). Statistical Tables for Biological, Agricultural and Medical Research. London: Oliver and Boyd.

Fleishman, A. E. (1980). Confidence intervals for correlation ratios. Educational and Psychological Measurement, 40, 659670.

Fletcher, R. (1969). Optimization. New York: Academic Press.

Fletcher, R., & Powell, M. J. D. (1963). A rapidly convergent descent method for minimization. Computer Journal, 6, 163-168.

Fletcher, R., & Reeves, C. M. (1964). Function minimization by conjugate gradients. Computer Journal, 7, 149-154.

Fomby, T.B., Hill, R.C., & Johnson, S.R. (1984). Advanced econometric methods. New York: Springer-Verlag.

Ford Motor Company, Ltd. & GEDAS (1991). Test examples for SPC software.

Fouladi, R. T. (1991). A comprehensive examination of procedures for testing the significance of a correlation matrix and its elements. Unpublished master's thesis, University of British Columbia, Vancouver, British Columbia, Canada.

Franklin, M. F. (1984). Constructing tables of minimum aberration p(nm) designs. Technometrics, 26, 225-232.

Fraser, C., & McDonald, R. P. (1988). COSAN: Covariance structure analysis. Multivariate Behavioral Research, 23, 263-265.

Freedman, L. S. (1982). Tables of the number of patients required in clinical trials using the logrank test. Statistics in Medicine, 1, 121129.

Friedman, J. (1991). Multivariate adaptive regression splines (with discussion), Annals of Statistics, 19, 1-141.

Friedman, J. H. (1993). Estimating functions of mixed ordinal and categorical variables using adaptive splines. in S. Morgenthaler, E. Ronchetti, & W. A. Stahel (Eds.) (1993, p. 73-113). New directions in statistical data analysis and robustness. Berlin: Birkhäuser Verlag.

Friedman, J. H. (1999a). Greedy function approximation: A gradient boosting machine. IMS 1999 Reitz Lecture.

Friedman, J. H. (1999b). Stochastic gradient boosting. Stanford University.

Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32, 675-701.

Friedman, M. (1940). A comparison of alternative tests of significance for the problem of m rankings. Annals of Mathematical Statistics, 11, 86-92.

Fries, A., & Hunter, W. G. (1980). Minimum aberration 2 (kp) designs. Technometrics, 22, 601-608.

Frost, P. A. (1975). Some properties of the Almon lag technique when one searches for degree of polynomial and lag. Journal of the American Statistical Association, 70, 606-612.

Fuller, W. A. (1976). Introduction to statistical time series. New York: Wiley.

Gaddum, J. H. (1945). Lognormal distributions. Nature, 156, 463-466.

Gale, N., & Halperin, W. C. (1982). A case for better graphics: The unclassed choropleth map. The American Statistician, 36, 330-336.

Galil, Z., & Kiefer, J. (1980). Time- and space-saving computer methods, related to Mitchell's DETMAX, for finding D-optimum designs. Technometrics, 22, 301-313.

Galton, F. (1882). Report of the anthropometric committee. In Report of the 51st Meeting of the British Association for the Advancement of Science, 1881, 245-260.

Galton, F. (1885). Section H. Anthropology. Opening address by Francis Galton. Nature, 32, 507- 510.

Galton, F. (1885). Some results of the anthropometric laboratory. Journal of the Anthropological Institute, 14, 275-287.

Galton, F. (1888). Co-relations and their measurement. Proceedings of the Royal Society of London, 45, 135-145.

Galton, F. (1889). Natural Inheritance. London: Macmillan.

Galton, F. (1889). Natural Inheritance. London: Macmillan.

Ganguli, M. (1941). A note on nested sampling. Sankhya, 5, 449-452.

Gara, M. A., & Rosenberg, S. (1979). The identification of persons as supersets and subsets in free-response personality descriptions. Journal of Personality and Social Psychology, 37, 2161-2170.

Gara, M. A., & Rosenberg, S. (1981). Linguistic factors in implicit personality theory. Journal of Personality and Social Psychology, 41, 450-457.

Gardner, E. S., Jr. (1985). Exponential smoothing: The state of the art. Journal of Forecasting, 4, 1-28.

Garthwaite, P. H. (1994) An Interpretation of Partial Least Squares, Journal of the American Statistical Association, 89 NO. 425, 122-127.

Garvin, D. A. (1987). Competing on the eight dimensions of quality. Harvard Business Review, November/December, 101-109.

Gatsonis, C., & Sampson, A. R. (1989). Multiple correlation: exact power and sample size calculations. Psychological Bulletin, 106, 516524.

Gbur, E., Lynch, M., & Weidman, L. (1986). An analysis of nine rating criteria on 329 U. S. metropolitan areas. Proceedings of the Section on Statistical Graphics, American Statistical Association, 104-109.

Gedye, R. (1968). A manager's guide to quality and reliability. New York: Wiley.

Gehan, E. A. (1965a). A generalized Wilcoxon test for comparing arbitrarily singly-censored samples. Biometrika, 52, 203-223.

Gehan, E. A. (1965b). A generalized two-sample Wilcoxon test for doubly-censored data. Biometrika, 52, 650-653.

Gehan, E. A., & Siddiqui, M. M. (1973). Simple regression methods for survival time studies. Journal of the American Statistical Association, 68, 848-856.

Gehan, E. A., & Thomas, D. G. (1969). The performance of some two sample tests in small samples with and without censoring. Biometrika, 56, 127-132.

Geladi, P. and Kowalski, B. R. (1986) Partial Least Squares Regression: A Tutorial, Analytica Chimica Acta, 185, 1-17.

Gerald, C. F., & Wheatley, P. O. (1989). Applied numerical analysis (4th ed.). Reading, MA: Addison Wesley.

Gibbons, J. D. (1976). Nonparametric methods for quantitative analysis. New York: Holt, Rinehart, & Winston.

Gibbons, J. D. (1985). Nonparametric statistical inference (2nd ed.). New York: Marcel Dekker.

Gifi, A. (1981). Nonlinear multivariate analysis. Department of Data Theory, The University of Leiden. The Netherlands.

Gifi, A. (1990). Nonlinear multivariate analysis. New York: Wiley.

Gill, P. E., & Murray, W. (1972). Quasi-Newton methods for unconstrained optimization. Journal of the Institute of Mathematics and its Applications, 9, 91-108.

Gill, P. E., & Murray, W. (1974). Numerical methods for constrained optimization. New York: Academic Press.

Gini, C. (1911). Considerazioni sulle probabilita a posteriori e applicazioni al rapporto dei sessi nelle nascite umane. Studi Economico-Giuridici della Universita de Cagliari, Anno III, 133-171.

Glass, G V., & Hopkins, K. D. (1996). Statistical methods in education and psychology. Needham Heights, MA: Allyn & Bacon.

Glass, G. V., & Stanley, J. (1970). Statistical methods in education and Psychology. Englewood Cliffs, NJ: Prentice-Hall.

Glasser, M. (1967). Exponential survival with covariance. Journal of the American Statistical Association, 62, 561-568.

Gnanadesikan, R., Roy, S., & Srivastava, J. (1971). Analysis and design of certain quantitative multiresponse experiments. Oxford: Pergamon Press, Ltd.

Goldberg, D. E. (1989). Genetic Algorithms. Reading, MA: Addison Wesley.

Golub, G. and Kahan, W. (1965). Calculating the singular values and pseudo-inverse of a matrix. SIAM Numerical Analysis, B 2 (2), 205-224.

Golub, G. H. and van Load, C. F. (1996) Matrix Computations, The Johns Hopkins University Press

Golub, G. H., & Van Loan, C. F. (1983). Matrix computations. Baltimore: Johns Hopkins University Press.

Gompertz, B. (1825). On the nature of the function expressive of the law of human mortality. Philosophical Transactions of the Royal Society of London, Series A, 115, 513-580.

Goodman, L .A., & Kruskal, W. H. (1972). Measures of association for cross-classifications IV: Simplification of asymptotic variances. Journal of the American Statistical Association, 67, 415-421.

Goodman, L. A. (1954). Kolmogorov-Smirnov tests for psychological research. Psychological Bulletin, 51, 160-168.

Goodman, L. A. (1971). The analysis of multidimensional contingency tables: Stepwise procedures and direct estimation methods for models building for multiple classification. Technometrics, 13, 33-61.

Goodman, L. A., & Kruskal, W. H. (1954). Measures of association for cross-classifications. Journal of the American Statistical Association, 49, 732-764.

Goodman, L. A., & Kruskal, W. H. (1959). Measures of association for cross-classifications II: Further discussion and references. Journal of the American Statistical Association, 54, 123-163.

Goodman, L. A., & Kruskal, W. H. (1963). Measures of association for cross-classifications III: Approximate sampling theory. Journal of the American Statistical Association, 58, 310-364.

Goodnight, J. H. (1980). Tests of hypotheses in fixed effects linear models. Communications in Statistics, A9, 167-180.

Gorman, R.P., & Sejnowski, T.J. (1988). Analysis of hidden units in a layered network trained to classify sonar targets. Neural Networks 1 (1), 75-89.

Grant, E. L., & Leavenworth, R. S. (1980). Statistical quality control (5th ed.). New York: McGraw-Hill.

Green, P. E., & Carmone, F. J. (1970). Multidimensional scaling and related techniques in marketing analysis. Boston: Allyn & Bacon.

Green, P. J. & Silverman, B. W. (1994) Nonparametric regression and generalized linear models: A roughness penalty approach. New York: Chapman & Hall.

Greenacre, M. J. & Hastie, T. (1987). The geometric interpretation of correspondence analysis. Journal of the American Statistical Association, 82, 437-447.

Greenacre, M. J. (1984). Theory and applications of correspondence analysis. New York: Academic Press.

Greenacre, M. J. (1988). Correspondence analysis of multivariate categorical data by weighted least-squares. Biometrika, 75, 457-467.

Greenhouse, S. W., & Geisser, S. (1958). Extension of Box's results on the use of the F distribution in multivariate analysis. Annals of Mathematical Statistics, 29, 95-112.

Greenhouse, S. W., & Geisser, S. (1959). On methods in the analysis of profile data. Psychometrika, 24, 95-112.

Grizzle, J. E. (1965). The two-period change-over design and its use in clinical trials. Biometrics, 21, 467-480.

Gross, A. J., & Clark, V. A. (1975). Survival distributions: Reliability applications in the medical sciences. New York: Wiley.

Gruska, G. F., Mirkhani, K., & Lamberson, L. R. (1989). Non-Normal data Analysis. Garden City, MI: Multiface Publishing.

Gruvaeus, G., & Wainer, H. (1972). Two additions to hierarchical cluster analysis. The British Journal of Mathematical and Statistical Psychology, 25, 200-206.

Guttman, L. (1954). A new approach to factor analysis: the radex. In P. F. Lazarsfeld (Ed.), Mathematical thinking in the social sciences. New York: Columbia University Press.

Guttman, L. (1968). A general nonmetric technique for finding the smallest coordinate space for a configuration of points. Pyrometrical, 33, 469-506.

Guttman, L. B. (1977). What is not what in statistics. The Statistician, 26, 81107.

Haberman, S. J. (1972). Loglinear fit for contingency tables. Applied Statistics, 21, 218-225.

Haberman, S. J. (1974). The analysis of frequency data. Chicago: University of Chicago Press.

Hahn, G. J., & Shapiro, S. S. (1967). Statistical models in engineering. New York: Wiley.

Hakstian, A. R., Rogers, W. D., & Cattell, R. B. (1982). The behavior of numbers of factors rules with simulated data. Multivariate Behavioral Research, 17, 193-219.

Hald, A. (1949). Maximum likelihood estimation of the parameters of a normal distribution which is truncated at a known point. Skandinavisk Aktuarietidskrift, 1949, 119-134.

Hald, A. (1952). Statistical theory with engineering applications. New York: Wiley.

Han, J., Kamber, M. (2000). Data mining: Concepts and Techniques. New York: Morgan-Kaufman.

Han, J., Lakshmanan, L. V. S., & Pei, J. (2001). Scalable frequent-pattern mining methods: An overview. In T. Fawcett (Ed.). KDD 2001: Tutorial Notes of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: The Association for Computing Machinery.

Harlow, L. L., Mulaik, S. A., & Steiger, J. H. (Eds.) (1997). What if there were no significance tests. Mahwah, NJ: Lawrence Erlbaum Associates.

Harman, H. H. (1967). Modern factor analysis. Chicago: University of Chicago Press.

Harris, R. J. (1976). The invalidity of partitioned U tests in canonical correlation and multivariate analysis of variance. Multivariate Behavioral Research, 11, 353-365.

Harrison, D. & Rubinfeld, D. L. (1978). Hedonic prices and the demand for clean air. Journal of Environmental Economics and Management, 5, 81-102.

Hart, K. M., & Hart, R. F. (1989). Quantitative methods for quality improvement. Milwaukee, WI: ASQC Quality Press.

Hartigan, J. A. & Wong, M. A. (1978). Algorithm 136. A k-means clustering algorithm. Applied Statistics, 28, 100.

Hartigan, J. A. (1975). Clustering algorithms. New York: Wiley.

Hartley, H. O. (1959). Smallest composite designs for quadratic response surfaces. Biometrics, 15, 611-624.

Harville, D. A. (1977). Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Association, 72, 320-340.

Haskell, A. C. (1922). Graphic Charts in Business. New York: Codex.

Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning : Data mining, inference, and prediction. New York: Springer.

Haviland, R. P. (1964). Engineering reliability and long life design. Princeton, NJ: Van Nostrand.

Hayduk, L. A. (1987). Structural equation modelling with LISREL: Essentials and advances. Baltimore: The Johns Hopkins University Press.

Haykin, S. (1994). Neural Networks: A Comprehensive Foundation. New York: Macmillan Publishing.

Haykin, S. (1994). Neural Networks: A Comprehensive Foundation. New York: Macmillan College Publishing.

Hays, W. L. (1981). Statistics (3rd ed.). New York: CBS College Publishing.

Hays, W. L. (1988). Statistics (4th ed.). New York: CBS College Publishing.

Heiberger, R. M. (1989). Computation for the analysis of designed experiments. New York: Wiley.

Hemmerle, W. J., & Hartley, H., O. (1973). Computing maximum likelihood estimates for the mixed A.O.V. model using the W transformation. Technometrics, 15, 819-831.

Henley, E. J., & Kumamoto, H. (1980). Reliability engineering and risk assessment. New York: Prentice-Hall.

Hettmansperger, T. P. (1984). Statistical inference based on ranks. New York: Wiley.

Hibbs, D. (1974). Problems of statistical estimation and causal inference in dynamic time series models. In H. Costner (Ed.), Sociological Methodology 1973/1974 (pp. 252-308). San Francisco: Jossey-Bass.

Hill, I. D., Hill, R., & Holder, R. L. (1976). Fitting Johnson curves by moments. Applied Statistics. 25, 190-192.

Hilton, T. L. (1969). Growth study annotated bibliography. Princeton, NJ: Educational Testing Service Progress Report 69-11.

Hochberg, J., & Krantz, D. H. (1986). Perceptual properties of statistical graphs. Proceedings of the Section on Statistical Graphics, American Statistical Association, 29-35.

Hocking, R. R. (1985). The analysis of linear models. Monterey, CA: Brooks/Cole.

Hocking, R. R. (1996). Methods and Applications of Linear Models. Regression and the Analysis of Variance. New York: Wiley.

Hocking, R. R., & Speed, F. M. (1975). A full rank analysis of some linear model problems. Journal of the American Statistical Association, 70, 707-712.

Hoerl, A. E. (1962). Application of ridge analysis to regression problems. Chemical Engineering Progress, 58, 54-59.

Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Applications to nonorthogonal problems. Technometrics, 12, 69-82.

Hoff, J. C. (1983). A practical guide to Box-Jenkins forecasting. London: Lifetime Learning Publications.

Hoffman, D. L. & Franke, G. R. (1986). Correspondence analysis: Graphical representation of categorical data in marketing research. Journal of Marketing Research, 13, 213-227.

Hogg, R. V., & Craig, A. T. (1970). Introduction to mathematical statistics. New York: Macmillan.

Holzinger, K. J., & Swineford, F. (1939). A study in factor analysis: The stability of a bi-factor solution. University of Chicago: Supplementary Educational Monographs, No. 48.

Hooke, R., & Jeeves, T. A. (1961). Direct search solution of numerical and statistical problems. Journal of the Association for Computing Machinery, 8, 212-229.

Hosmer, D. W and Lemeshow, S. (1989), Applied Logistic Regression, John Wiley & Sons, Inc.

Hotelling, H. (1947). Multivariate quality control. In Eisenhart, Hastay, and Wallis (Eds.), Techniques of Statistical Analysis. New York: McGraw-Hill.

Hotelling, H., & Pabst, M. R. (1936). Rank correlation and tests of significance involving no assumption of normality. Annals of Mathematical Statistics, 7, 29-43.

Hoyer, W., & Ellis, W. C. (1996). A graphical exploration of SPC. Quality Progress, 29, 65-73.

Hsu, P. L. (1938). Contributions to the theory of Student's t test as applied to the problem of two samples. Statistical Research Memoirs, 2, 1-24.

Huba, G. J., & Harlow, L. L. (1987). Robust structural equation models: implications for developmental psychology. Child Development, 58, 147-166.

Huberty, C. J. (1975). Discriminant analysis. Review of Educational Research, 45, 543-598.

Hunter, A., Kennedy, L., Henry, J., & Ferguson, R.I. (2000). Application of Neural Networks and Sensitivity Analysis to improved prediction of Trauma Survival. Computer Methods and Algorithms in Biomedicine 62, 11-19.

Huynh, H., & Feldt, L. S. (1970). Conditions under which mean square ratios in repeated measures designs have exact F-distributions. Journal of the American Statistical Association, 65, 1582-1589.

Ireland, C. T., & Kullback, S. (1968). Contingency tables with given marginals. Biometrika, 55, 179-188.

Jaccard, J., Weber, J., & Lundmark, J. (1975). A multitrait-multimethod factor analysis of four attitude assessment procedures. Journal of Experimental Social Psychology, 11, 149-154.

Jacobs, D. A. H. (Ed.). (1977). The state of the art in numerical analysis. London: Academic Press.

Jacobs, R.A. (1988). Increased Rates of Convergence Through Learning Rate Adaptation. Neural Networks 1 (4), 295-307.

Jacoby, S. L. S., Kowalik, J. S., & Pizzo, J. T. (1972). Iterative methods for nonlinear optimization problems. Englewood Cliffs, NJ: Prentice-Hall.

James, L. R., Mulaik, S. A., & Brett, J. M. (1982). Causal analysis. Assumptions, models, and data. Beverly Hills, CA: Sage Publications.

Jardine, N., & Sibson, R. (1971). Mathematical taxonomy. New York: Wiley.

Jastrow, J. (1892). On the judgment of angles and position of lines. American Journal of Psychology, 5, 214-248.

Jenkins, G. M., & Watts, D. G. (1968). Spectral analysis and its applications. San Francisco: Holden-Day.

Jennrich, R. I. (1970). An asymptotic test for the equality of two correlation matrices. Journal of the American Statistical Association, 65, 904-912.

Jennrich, R. I. (1977). Stepwise regression. In K. Enslein, A. Ralston, & H.S. Wilf (Eds.), Statistical methods for digital computers. New York: Wiley.

Jennrich, R. I., & Moore, R. H. (1975). Maximum likelihood estimation by means of nonlinear least squares. Proceedings of the Statistical Computing Section, American Statistical Association, 57-65.

Jennrich, R. I., & Sampson, P. F. (1968). Application of stepwise regression to non-linear estimation. Technometrics, 10, 63-72.

Jennrich, R. I., & Sampson, P. F. (1976). Newton-Raphson and related algorithms for maximum likelihood variance component estimation. Technometrics, 18, 11-17.

Jennrich, R. I., & Schuchter, M. D. (1986). Unbalanced repeated-measures models with structured covariance matrices. Biometrics, 42, 805-820.

Jennrich. R. I. (1977). Stepwise discriminant analysis. In K. Enslein, A. Ralston, & H.S. Wilf (Eds.), Statistical methods for digital computers. New York: Wiley.

Johnson, L. W., & Ries, R. D. (1982). Numerical Analysis (2nd ed.). Reading, MA: Addison Wesley.

Johnson, N. L. (1961). A simple theoretical approach to cumulative sum control charts. Journal of the American Statistical Association, 56, 83-92.

Johnson, N. L. (1965). Tables to facilitate fitting SU frequency curves. Biometrika, 52, 547.

Johnson, N. L., & Kotz, S. (1970). Continuous univariate distributions, Vol I and II. New York: Wiley.

Johnson, N. L., Kotz, S., Balakrishnan, N. (1995). Continuous univariate distributions: Volume II. (2nd Ed). NY: Wiley.

Johnson, N. L., & Leone, F. C. (1962). Cumulative sum control charts - mathematical principles applied to their construction and use. Industrial Quality Control, 18, 15-21.

Johnson, N. L., Nixon, E., & Amos, D. E. (1963). Table of percentage points of pearson curves. Biometrika, 50, 459.

Johnson, N. L., Nixon, E., Amos, D. E., & Pearson, E. S. (1963). Table of percentage points of Pearson curves for given 1 and 2, expressed in standard measure. Biometrika, 50, 459-498.

Johnson, P. (1987). SPC for short runs: A programmed instruction workbook. Southfield, MI: Perry Johnson.

Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32, 241-254.

Johnston, J. (1972). Econometric methods. New York: McGraw-Hill.

Jöreskog, K. G. (1973). A general model for estimating a linear structural equation system. In A. S. Goldberger and O. D. Duncan (Eds.), Structural Equation Models in the Social Sciences. New York: Seminar Press.

Jöreskog, K. G. (1974). Analyzing psychological data by structural analysis of covariance matrices. In D. H. Krantz, R. C. Atkinson, R. D. Luce, and P. Suppes (Eds.), Contemporary Developments in Mathematical Psychology, Vol. II. New York: W. H. Freeman and Company.

Jöreskog, K. G. (1978). Structural analysis of covariance and correlation matrices. Psychometrika, 43, 443-477.

Jöreskog, K. G., & Lawley, D. N. (1968). New methods in maximum likelihood factor analysis. British Journal of Mathematical and Statistical Psychology, 21, 85-96.

Jöreskog, K. G., & Sörbom, D. (1979). Advances in factor analysis and structural equation models. Cambridge, MA: Abt Books.

Jöreskog, K. G., & Sörbom, D. (1982). Recent developments in structural equation modeling. Journal of Marketing Research, 19, 404-416.

Jöreskog, K. G., & Sörbom, D. (1984). Lisrel VI. Analysis of linear structural relationships by maximum likelihood, instrumental variables, and least squares methods. Mooresville, Indiana: Scientific Software.

Jöreskog, K. G., & Sörbom, D. (1989). Lisrel 7. A guide to the program and applications. Chicago, Illinois: SPSS Inc.

Judge, G. G., Griffith, W. E., Hill, R. C., Luetkepohl, H., & Lee, T. S. (1985). The theory and practice of econometrics. New York: Wiley.

Juran, J. M. (1960). Pareto, Lorenz, Cournot, Bernoulli, Juran and others. Industrial Quality Control, 17, 25.

Juran, J. M. (1962). Quality control handbook. New York: McGraw-Hill.

Juran, J. M., & Gryna, F. M. (1970). Quality planning and analysis. New York: McGraw-Hill.

Juran, J. M., & Gryna, F. M. (1980). Quality planning and analysis (2nd ed.). New York: McGraw-Hill.

Juran, J. M., & Gryna, F. M. (1988). Juran's quality control handbook (4th ed.). New York: McGraw-Hill.

Kachigan, S. K. (1986). Statistical analysis: An interdisciplinary introduction to univariate & multivariate methods. New York: Radius Press.

Kackar, R. M. (1985). Off-line quality control, parameter design, and the Taguchi method. Journal of Quality Technology, 17, 176-188.

Kackar, R. M. (1986). Taguchi's quality philosophy: Analysis and commentary. Quality Progress, 19, 21-29.

Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases. New York: Cambridge University Press.

Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Pyrometrical, 23, 187-200.

Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, 141-151.

Kalbfleisch, J. D., & Prentice, R. L. (1980). The statistical analysis of failure time data. New York: Wiley.

Kane, V. E. (1986). Process capability indices. Journal of Quality Technology, 18, 41-52.

Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53, 457-481.

Karsten, K. G., (1925). Charts and graphs. New York: Prentice-Hall.

Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29, 119-127.

Keats, J. B., & Lawrence, F. P. (1997). Weibull maximum likelihood parameter estimates with censored data. Journal of Quality Technology, 29, 105-110.

Keeves, J. P. (1972). Educational environment and student achievement. Melbourne: Australian Council for Educational Research.

Kendall, M. G. (1940). Note on the distribution of quantiles for large samples. Supplement of the Journal of the Royal Statistical Society, 7, 83-85.

Kendall, M. G. (1948). Rank correlation methods. (1st ed.). London: Griffin.

Kendall, M. G. (1975). Rank correlation methods (4th ed.). London: Griffin.

Kendall, M. G. (1984). Time Series. New York: Oxford University Press.

Kendall, M., & Ord, J. K. (1990). Time series (3rd ed.). London: Griffin.

Kendall, M., & Stuart, A. (1977). The advanced theory of statistics. (Vol. 1). New York: MacMillan.

Kendall, M., & Stuart, A. (1979). The advanced theory of statistics (Vol. 2). New York: Hafner.

Kennedy, A. D., & Gehan, E. A. (1971). Computerized simple regression methods for survival time studies. Computer Programs in Biomedicine, 1, 235-244.

Kennedy, W. J., & Gentle, J. E. (1980). Statistical computing. New York: Marcel Dekker, Inc.

Kenny, D. A. (1979). Correlation and causality. New York: Wiley.

Keppel, G. (1973). Design and analysis: A researcher's handbook. Englewood Cliffs, NJ: Prentice-Hall.

Keppel, G. (1982). Design and analysis: A researcher's handbook (2nd ed.). Englewood Cliffs, NJ: Prentice-Hall.

Keselman, H. J., Rogan, J. C., Mendoza, J. L., & Breen, L. L. (1980). Testing the validity conditions for repeated measures F tests. Psychological Bulletin, 87, 479-481.

Khuri, A. I., & Cornell, J. A. (1987). Response surfaces: Designs and analyses. New York: Marcel Dekker, Inc.

Kiefer, J., & Wolfowitz, J. (1960). The equivalence of two extremum problems. Canadian Journal of Mathematics, 12, 363-366.

Kim, J. O., & Mueller, C. W. (1978a). Factor analysis: Statistical methods and practical issues. Beverly Hills, CA: Sage Publications.

Kim, J. O., & Mueller, C. W. (1978b). Introduction to factor analysis: What it is and how to do it. Beverly Hills, CA: Sage Publications.

Kirk, D. B. (1973). On the numerical approximation of the bivariate normal (tetrachoric) correlation coefficient. Psychometrika, 38, 259-268.

Kirk, R. E. (1968). Experimental design: Procedures for the behavioral sciences. (1st ed.). Monterey, CA: Brooks/Cole.

Kirk, R. E. (1982). Experimental design: Procedures for the behavioral sciences. (2nd ed.). Monterey, CA: Brooks/Cole.

Kirk, R. E. (1995). Experimental design: Procedures for the behavioral sciences. Pacific Grove, CA: Brooks-Cole.

Kirkpatrick, S., Gelatt, C.D. and Vecchi, M.P. (1983). Optimization by simulated annealing. Science 220 (4598), 671-680.

Kish, L. (1965). Survey sampling. New York: Wiley.

Kivenson, G. (1971). Durability and reliability in engineering design. New York: Hayden.

Klecka, W. R. (1980). Discriminant analysis. Beverly Hills, CA: Sage.

Klein, L. R. (1974). A textbook of econometrics. Englewood Cliffs, NJ: Prentice-Hall.

Kleinbaum, D. G. (1996). Survival analysis: A self-learning text. New York: Springer-Verlag.

Kline, P. (1979). Psychometrics and psychology. London: Academic Press.

Kline, P. (1986). A handbook of test construction. New York: Methuen.

Kmenta, J. (1971). Elements of econometrics. New York: Macmillan.

Knuth, Donald E. (1981). Seminumerical algorithms. 2nd ed., Vol 2 of: The art of computer programming. Reading, Mass.: Addison-Wesley.

Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 59-69.

Kohonen, T. (1990). Improved versions of learning vector quantization. International Joint Conference on Neural Networks 1, 545-550. San Diego, CA.

Kolata, G. (1984). The proper display of data. Science, 226, 156-157.

Kolmogorov, A. (1941). Confidence limits for an unknown distribution function. Annals of Mathematical Statistics, 12, 461-463.

Korin, B. P. (1969). On testing the equality of k covariance matrices. Biometrika, 56, 216-218.

Kramer, M.A. (1991). Nonlinear principal components analysis using autoassociative neural networks. AIChe Journal 37 (2), 233-243.

Kruskal, J. B. (1964). Nonmetric multidimensional scaling: A numerical method. Pyrometrical, 29, 1-27, 115-129.

Kruskal, J. B., & Wish, M. (1978). Multidimensional scaling. Beverly Hills, CA: Sage Publications.

Kruskal, W. H. (1952). A nonparametric test for the several sample problem. Annals of Mathematical Statistics, 23, 525-540.

Kruskal, W. H. (1975). Visions of maps and graphs. In J. Kavaliunas (Ed.), Auto-carto II, proceedings of the international symposium on computer assisted cartography. Washington, DC: U. S. Bureau of the Census and American Congress on Survey and Mapping.

Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47, 583-621.

Ku, H. H., & Kullback, S. (1968). Interaction in multidimensional contingency tables: An information theoretic approach. J. Res. Nat. Bur. Standards Sect. B, 72, 159-199.

Ku, H. H., Varner, R. N., & Kullback, S. (1971). Analysis of multidimensional contingency tables. Journal of the American Statistical Association, 66, 55-64.

Kullback, S. (1959). Information theory and statistics. New York: Wiley.

Kvålseth, T. O. (1985). Cautionary note about R2. The American Statistician, 39, 279-285.

Lagakos, S. W., & Kuhns, M. H. (1978). Maximum likelihood estimation for censored exponential survival data with covariates. Applied Statistics, 27, 190-197.

Lakatos, E., & Lan, K. K. G. (1992). A comparison of sample size methods for the logrank statistic. Statistics in Medicine, 11, 179191.

Lance, G. N., & Williams, W. T. (1966). A general theory of classificatory sorting strategies. Computer Journal, 9, 373.

Lance, G. N., & Williams, W. T. (1966). Computer programs for hierarchical polythetic classification ("symmetry analysis"). Computer Journal, 9, 60.

Larsen, W. A., & McCleary, S. J. (1972). The use of partial residual plots in regression analysis. Technometrics, 14, 781-790.

Lawless, J. F. (1982). Statistical models and methods for lifetime data. New York: Wiley.

Lawley, D. N., & Maxwell, A. E. (1971). Factor analysis as a statistical method. New York: American Elsevier.

Lawley, D. N., & Maxwell, A. E. (1971). Factor analysis as a statistical method (2nd. ed.). London: Butterworth & Company.

Lebart, L., Morineau, A., and Tabard, N. (1977). Techniques de la description statistique. Paris: Dunod.

Lebart, L., Morineau, A., and Warwick, K., M. (1984). Multivariate descriptive statistical analysis: Correspondence analysis and related techniques for large matrices. New York: Wiley.

Lee, E. T. (1980). Statistical methods for survival data analysis. Belmont, CA: Lifetime Learning.

Lee, E. T., & Desu, M. M. (1972). A computer program for comparing K samples with right-censored data. Computer Programs in Biomedicine, 2, 315-321.

Lee, E. T., Desu, M. M., & Gehan, E. A. (1975). A Monte-Carlo study of the power of some two-sample tests. Biometrika, 62, 425-532.

Lee, S., & Hershberger, S. (1990). A simple rule for generating equivalent models in covariance structure modeling. Multivariate Behavioral Research, 25, 313-334.

Lee, Y. S. (1972). Tables of upper percentage points of the multiple correlation coefficient. Biometrika, 59, 175189.

Legendre, A. M. (1805). Nouvelles Methodes pour la Determination des Orbites des Cometes. Paris: F. Didot.

Lehmann, E. L. (1975). Nonparametrics: Statistical methods based on ranks. San Francisco: Holden-Day.

Levenberg, K. (1944). A method for the solution of certain non-linear problems in least squares. Quarterly Journal of Applied Mathematics II (2), 164-168.

Lewicki, P., Hill, T., & Czyzewska, M. (1992). Nonconscious acquisition of information. American Psychologist, 47, 796-801.

Lieblein, J. (1953). On the exact evaluation of the variances and covariances of order statistics in samples form the extreme-value distribution. Annals of Mathematical Statistics, 24, 282-287.

Lieblein, J. (1955). On moments of order statistics from the Weibull distribution. Annals of Mathematical Statistics, 26, 330-333.

Lilliefors, H. W. (1967). On the Kolmogorov-Smirnov test for normality with mean and variance unknown. Journal of the American Statistical Association, 64, 399-402.

Lim, T.-S., Loh, W.-Y., & Shih, Y.-S. (1997). An empirical comparison of decision trees and other classification methods. Technical Report 979, Department of Statistics, University of Wisconsin, Madison.

Lindeman, R. H., Merenda, P. F., & Gold, R. (1980). Introduction to bivariate and multivariate analysis. New York: Scott, Foresman, & Co.

Lindman, H. R. (1974). Analysis of variance in complex experimental designs. San Francisco: W. H. Freeman & Co.

Linfoot, E. H. (1957). An informational measure of correlation. Information and Control, 1, 50-55.

Linn, R. L. (1968). A Monte Carlo approach to the number of factors problem. Psychometrika, 33, 37-71.

Lipson, C., & Sheth, N. C. (1973). Statistical design and analysis of engineering experiments. New York: McGraw-Hill.

Lloyd, D. K., & Lipow, M. (1977). Reliability: Management, methods, and mathematics. New York: McGraw-Hill.

Loehlin, J. C. (1987). Latent variable models: An introduction to latent, path, and structural analysis. Hillsdale, NJ: Erlbaum.

Loh, W.-Y, & Shih, Y.-S. (1997). Split selection methods for classification trees. Statistica Sinica, 7, 815-840.

Loh, W.-Y., & Vanichestakul, N. (1988). Tree-structured classification via generalized discriminant analysis (with discussion). Journal of the American Statistical Association, 83, 715-728.

Long, J. S. (1983a). Confirmatory factor analysis. Beverly Hills: Sage.

Long, J. S. (1983b). Covariance structure models: An introduction to LISREL. Beverly Hills: Sage.

Longley, J. W. (1967). An appraisal of least squares programs for the electronic computer from the point of view of the user. Journal of the American Statistical Association, 62, 819-831.

Longley, J. W. (1984). Least squares computations using orthogonalization methods. New York: Marcel Dekker.

Lord, F. M. (1957). A significance test for the hypothesis that two variables measure the same trait except for errors of measurement. Psychometrika, 22, 207-220.

Lorenz, M. O. (1904). Methods of measuring the concentration of wealth. American Statistical Association Publication, 9, 209-219.

Lowe, D. (1989). Adaptive radial basis function non-linearities, and the problem of generalisation. First IEEE International Conference on Artificial Neural Networks, 171-175, London, UK.

Lucas, J. M. (1976). The design and use of cumulative sum quality control schemes. Journal of Quality Technology, 8, 45-70.

Lucas, J. M. (1982). Combined Shewhart-CUSUM quality control schemes. Journal of Quality Technology, 14, 89-93.

MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structur modeling. Psychological Methods, 1, 130149.

Maddala, G. S. (1977) Econometrics. New York: McGraw-Hill.

Maiti, S. S., & Mukherjee, B. N. (1990). A note on the distributional properties of the Jöreskog-Sörbom fit indices. Psychometrika, 55, 721-726.

Makridakis, S. G. (1983). Empirical evidence versus personal experience. Journal of Forecasting, 2, 295-306.

Makridakis, S. G. (1990). Forecasting, planning, and strategy for the 21st century. London: Free Press.

Makridakis, S. G., & Wheelwright, S. C. (1978). Interactive forecasting: Univariate and multivariate methods (2nd ed.). San Francisco, CA: Holden-Day.

Makridakis, S. G., & Wheelwright, S. C. (1989). Forecasting methods for management (5th ed.). New York: Wiley.

Makridakis, S. G., Wheelwright, S. C., & McGee, V. E. (1983). Forecasting: Methods and applications (2nd ed.). New York: Wiley.

Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen, R., & Winkler, R. (1982). The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting, 1, 11-153.

Malinvaud, E. (1970). Statistical methods of econometrics. Amsterdam: North-Holland Publishing Co.

Mandel, B. J. (1969). The regression control chart. Journal of Quality Technology, 1, 3-10.

Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18, 50-60.

Mann, N. R., Schafer, R. E., & Singpurwalla, N. D. (1974). Methods for statistical analysis of reliability and life data. New York: Wiley.

Mann, N. R., Scheuer, R. M, & Fertig, K. W. (1973). A new goodness of fit test for the two-parameter Weibull or extreme value distribution. Communications in Statistics, 2, 383-400.

Mantel, N. (1966). Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemotherapy Reports, 50, 163-170.

Mantel, N. (1967). Ranking procedures for arbitrarily restricted observations. Biometrics, 23, 65-78.

Mantel, N. (1974). Comment and suggestion on the Yates continuity correction. Journal of the American Statistical Association, 69, 378-380.

Mantel, N., & Haenszel, W. (1959). Statistical aspects of the analysis of data from retrospective studies of disease. Journal of the National Cancer Institute, 22, 719-748.

Marascuilo, L. A., & McSweeney, M. (1977). Nonparametric and distribution free methods for the social sciences. Monterey, CA: Brooks/Cole.

Marple, S. L., Jr. (1987). Digital spectral analysis. Englewood Cliffs, NJ: Prentice-Hall.

Marquardt, D.W. (1963). An algorithm for least-squares estimation of non-linear parameters. Journal of the Society of Industrial and Applied Mathematics 11 (2), 431-441.

Marsaglia, G. (1962). Random variables and computers. In J. Kozenik (Ed.), Information theory, statistical decision functions, random processes: Transactions of the third Prague Conference. Prague: Czechoslovak Academy of Sciences.

Mason, R. L., Gunst, R. F., & Hess, J. L. (1989). Statistical design and analysis of experiments with applications to engineering and science. New York: Wiley.

Massey, F. J., Jr. (1951). The Kolmogorov-Smirnov test for goodness of fit. Journal of the American Statistical Association, 46, 68-78.

Masters (1995). Neural, Novel, and Hybrid Algorithms for Time Series Predictions. New York: Wiley.

Matsueda, R. L., & Bielby, W. T. (1986). Statistical power in covariance structure models. In N. B. Tuma (Ed.), Sociological methodology. Washington, DC: American Sociological Association.

McArdle, J. J. (1978). A structural view of structural models. Paper presented at the Winter Workshop on Latent Structure Models Applied to Developmental Data, University of Denver, December, 1978.

McArdle, J. J., & McDonald, R. P. (1984). Some algebraic properties of the Reticular Action Model for moment structures. British Journal of Mathematical and Statistical Psychology, 37, 234-251.

McCleary, R., & Hay, R. A. (1980). Applied time series analysis for the social sciences. Beverly Hills, CA: Sage Publications.

McCullagh, P. & Nelder, J. A. (1989). Generalized linear models (2nd Ed.). New York: Chapman & Hall.

McDonald, R. P. (1980). A simple comprehensive model for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 31, 59-72.

McDonald, R. P. (1989). An index of goodness-of-fit based on noncentrality. Journal of Classification, 6, 97-103.

McDonald, R. P., & Hartmann, W. M. (1992). A procedure for obtaining initial value estimates in the RAM model. Multivariate Behavioral Research, 27, 57-76.

McDonald, R. P., & Mulaik, S. A. (1979). Determinacy of common factors: A nontechnical review. Psychological Bulletin, 86, 297-306.

McDowall, D., McCleary, R., Meidinger, E. E., & Hay, R. A. (1980). Interrupted time series analysis. Beverly Hills, CA: Sage Publications.

McKenzie, E. (1984). General exponential smoothing and the equivalent ARMA process. Journal of Forecasting, 3, 333-344.

McKenzie, E. (1985). Comments on 'Exponential smoothing: The state of the art' by E. S. Gardner, Jr. Journal of Forecasting, 4, 32-36.

McLachlan, G. J. (1992). Discriminant analysis and statistical pattern recognition. New York: Wiley.

McLain, D. H. (1974). Drawing contours from arbitrary data points. The Computer Journal, 17, 318-324.

McLean, R. A., & Anderson, V. L. (1984). Applied factorial and fractional designs. New York: Marcel Dekker.

McLeod, A. I., & Sales, P. R. H. (1983). An algorithm for approximate likelihood calculation of ARMA and seasonal ARMA models. Applied Statistics, 211-223 (Algorithm AS).

McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12, 153-157.

McNemar, Q. (1969). Psychological statistics (4th ed.). New York: Wiley.

Melard, G. (1984). A fast algorithm for the exact likelihood of autoregressive-moving average models. Applied Statistics, 33, 104-119.

Mels, G. (1989). A general system for path analysis with latent variables. M. S. Thesis: Department of Statistics, University of South Africa.

Mendoza, J. L., Markos, V. H., & Gonter, R. (1978). A new perspective on sequential testing procedures in canonical analysis: A Monte Carlo evaluation. Multivariate Behavioral Research, 13, 371-382.

Meredith, W. (1964). Canonical correlation with fallible data. Psychometrika, 29, 55-65.

Miettinnen, O. S. (1968). The matched pairs design in the case of all-or-none responses. Biometrics, 24, 339352.

Miller, R. (1981). Survival analysis. New York: Wiley.

Milligan, G. W. (1980). An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Psychometrika, 45, 325-342.

Milliken, G. A., & Johnson, D. E. (1984). Analysis of messy data: Vol. I. Designed experiments. New York: Van Nostrand Reinhold, Co.

Milliken, G. A., & Johnson, D. E. (1992). Analysis of messy data: Vol. I. Designed experiments. New York: Chapman & Hall.

Minsky, M.L. and Papert, S.A. (1969). Perceptrons. Cambridge, MA: MIT Press.

Mitchell, T. J. (1974a). Computer construction of "D-optimal" first-order designs. Technometrics, 16, 211-220.

Mitchell, T. J. (1974b). An algorithm for the construction of "D-optimal" experimental designs. Technometrics, 16, 203-210.

Mittag, H. J. (1993). Qualitätsregelkarten. München/Wien: Hanser Verlag.

Mittag, H. J., & Rinne, H. (1993). Statistical methods of quality assurance. London/New York: Chapman & Hall.

Monro, D. M. (1975). Complex discrete fast Fourier transform. Applied Statistics, 24, 153-160.

Monro, D. M., & Branch, J. L. (1976). The chirp discrete Fourier transform of general length. Applied Statistics, 26, 351-361.

Montgomery, D. C. (1976). Design and analysis of experiments. New York: Wiley.

Montgomery, D. C. (1985). Statistical quality control. New York: Wiley.

Montgomery, D. C. (1991) Design and analysis of experiments (3rd ed.). New York: Wiley.

Montgomery, D. C. (1996). Introduction to Statistical Quality Control (3rd Edition). New York:Wiley.

Montgomery, D. C. (1996). Statistical quality control (3rd. Edition). New York: Wiley.

Montgomery, D. C., & Wadsworth, H. M. (1972). Some techniques for multivariate quality control applications. Technical Conference Transactions. Washington, DC: American Society for Quality Control.

Montgomery, D. C., Johnson, L. A., & Gardiner, J. S. (1990). Forecasting and time series analysis (2nd ed.). New York: McGraw-Hill.

Mood, A. M. (1954). Introduction to the theory of statistics. New York: McGraw Hill.

Moody, J. and Darkin, C.J. (1989). Fast learning in networks of locally-tuned processing units. Neural Computation 1 (2), 281-294.

Moré, J. J., (1977). The Levenberg-Marquardt Algorithm: Implementation and Theory. In G.A. Watson, (ed.), Lecture Notes in Mathematics 630, p. 105-116. Berlin: Springer-Verlag.

Morgan, J. N., & Messenger, R. C. (1973). THAID: A sequential analysis program for the analysis of nominal scale dependent variables. Technical report, Institute of Social Research, University of Michigan, Ann Arbor.

Morgan, J. N., & Sonquist, J. A. (1963). Problems in the analysis of survey data, and a proposal. Journal of the American Statistical Association, 58, 415-434.

Morris, M., & Thisted, R. A. (1986). Sources of error in graphical perception: A critique and an experiment. Proceedings of the Section on Statistical Graphics, American Statistical Association, 43-48.

Morrison, A. S., Black, M. M., Lowe, C. R., MacMahon, B., & Yuasa, S. (1973). Some international differences in histology and survival in breast cancer. International Journal of Cancer, 11, 261-267.

Morrison, D. (1967). Multivariate statistical methods. New York: McGraw-Hill.

Morrison, D. F. (1990). Multivariate statistical methods. (3rd Ed.). New York: McGraw-Hill.

Moses, L. E. (1952). Non-parametric statistics for psychological research. Psychological Bulletin, 49, 122-143.

Mulaik, S. A. (1972). The foundations of factor analysis. New York: McGraw Hill.

Murphy, K. R., & Myors, B. (1998). Statistical power analysis: A simple general model for traditional and modern hypothesis tests. Mahwah, NJ: Lawrence Erlbaum Associates.

Muth, J. F. (1960). Optimal properties of exponentially weighted forecasts. Journal of the American Statistical Association, 55, 299-306.

Nachtsheim, C. J. (1979). Contributions to optimal experimental design. Ph.D. thesis, Department of Applied Statistics, University of Minnesota.

Nachtsheim, C. J. (1987). Tools for computer-aided design of experiments. Journal of Quality Technology, 19, 132-160.

Nelder, J. A., & Mead, R. (1965). A Simplex method for function minimization. Computer Journal, 7, 308-313.

Nelson, L. (1984). The Shewhart control chart - tests for special causes. Journal of Quality Technology, 15, 237-239.

Nelson, L. (1985). Interpreting Shewhart X-bar control charts. Journal of Quality Technology, 17, 114-116.

Nelson, W. (1982). Applied life data analysis. New York: Wiley.

Nelson, W. (1990). Accelerated testing: Statistical models, test plans, and data analysis. New York: Wiley.

Neter, J., Wasserman, W., & Kutner, M. H. (1985). Applied linear statistical models: Regression, analysis of variance, and experimental designs. Homewood, IL: Irwin.

Neter, J., Wasserman, W., & Kutner, M. H. (1989). Applied linear regression models (2nd ed.). Homewood, IL: Irwin.

Newcombe, Robert G. (1998). Two-sided confidence intervals for the single proportion: comparison of seven methods. Statistics in Medicine, 17, 857872.

Neyman, J., & Pearson, E. S. (1931). On the problem of k samples. Bulletin de l'Academie Polonaise des Sciences et Lettres, Ser. A, 460-481.

Neyman, J., & Pearson, E. S. (1933). On the problem of the most efficient tests of statistical hypothesis. Philosophical Transactions of the Royal Society of London, Ser. A, 231, 289-337.

Nisbett, R. E., Fong, G. F., Lehman, D. R., & Cheng, P. W. (1987). Teaching reasoning. Science, 238, 625-631.

Noori, H. (1989). The Taguchi methods: Achieving design and output quality. The Academy of Management Executive, 3, 322-326.

Nunnally, J. C. (1970). Introduction to psychological measurement. New York: McGraw-Hill.

Nunnally, J. C. (1978). Psychometric theory. New York: McGraw-Hill.

Nussbaumer, H. J. (1982). Fast Fourier transforms and convolution algorithms (2nd ed.). New York: Springer-Verlag.

O'Brien, R. G., & Kaiser, M. K. (1985). MANOVA method for analyzing repeated measures designs: An extensive primer. Psychological Bulletin, 97, 316-333.

Okunade, A. A., Chang, C. F., & Evans, R. D. (1993). Comparative analysis of regression output summary statistics in common statistical packages. The American Statistician, 47, 298-303.

Olds, E. G. (1949). The 5% significance levels for sums of squares of rank differences and a correction. Annals of Mathematical Statistics, 20, 117-118.

Olejnik, S. F., & Algina, J. (1987). Type I error rates and power estimates of selected parametric and nonparametric tests of scale. Journal of Educational Statistics, 12, 45-61.

Olson, C. L. (1976). On choosing a test statistic in multivariate analysis of variance. Psychological Bulletin, 83, 579-586.

O'Neill, R. (1971). Function minimization using a Simplex procedure. Applied Statistics, 3, 79-88.

Ostle, B., & Malone, L. C. (1988). Statistics in research: Basic concepts and techniques for research workers (4th ed.). Ames, IA: Iowa State Press.

Ostrom, C. W. (1978). Time series analysis: Regression techniques. Beverly Hills, CA: Sage Publications.

Overall, J. E., & Spiegel, D. K. (1969). Concerning least squares analysis of experimental data. Psychological Bulletin, 83, 579-586.

Page, E. S. (1954). Continuous inspection schemes. Biometrics, 41, 100-114.

Page, E. S. (1961). Cumulative sum charts. Technometrics, 3, 1-9.

Palumbo, F. A., & Strugala, E. S. (1945). Fraction defective of battery adapter used in handie-talkie. Industrial Quality Control, November, 68.

Pankratz, A. (1983). Forecasting with univariate Box-Jenkins models: Concepts and cases. New York: Wiley.

Parker, D.B. (1985). Learning logic. Technical Report TR-47, Cambridge, MA: MIT Center for Research in Computational Economics and Management Science.

Parzen, E. (1961). Mathematical considerations in the estimation of spectra: Comments on the discussion of Messers, Tukey, and Goodman. Technometrics, 3, 167-190; 232-234.

Parzen, E. (1962). On estimation of a probability density function and mode. Annals of Mathematical Statistics 33, 1065-1076.

Patil, K. D. (1975). Cochran's Q test: Exact distribution. Journal of the American Statistical Association, 70, 186-189.

Patterson, D. (1996). Artificial Neural Networks. Singapore: Prentice Hall.

Peace, G. S. (1993). Taguchi methods: A hands-on approach. Milwaukee, Wisconsin: ASQC.

Pearson, E. S., and Hartley, H. O. (1972). Biometrika tables for statisticians, Vol II. Cambridge: Cambridge University Press.

Pearson, K. (1894). Contributions to the mathematical theory of evolution. Philosophical Transactions of the Royal Society of London, Ser. A, 185, 71-110.

Pearson, K. (1895). Skew variation in homogeneous material. Philosophical Transactions of the Royal Society of London, Ser. A, 186, 343-414.

Pearson, K. (1896). Regression, heredity, and panmixia. Philosophical Transactions of the Royal Society of London, Ser. A, 187, 253-318.

Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 5th Ser., 50, 157-175.

Pearson, K. (1904). On the theory of contingency and its relation to association and normal correlation. Drapers' Company Research Memoirs, Biometric Ser. I.

Pearson, K. (1905). Das Fehlergesetz und seine Verallgemeinerungen durch Fechner und Pearson. A Rejoinder. Biometrika, 4, 169-212.

Pearson, K. (1908). On the generalized probable error in multiple normal correlation. Biometrika, 6, 59-68.

Pearson, K., (Ed.). (1968). Tables of incomplete beta functions (2nd ed.). Cambridge, MA: Cambridge University Press.

Pedhazur, E. J. (1973). Multiple regression in behavioral research. New York: Holt, Rinehart, & Winston.

Pedhazur, E. J. (1982). Multiple regression in behavioral research (2nd ed.). New York: Holt, Rinehart, & Winston.

Peressini, A. L., Sullivan, F. E., & Uhl, J. J., Jr. (1988). The mathematics of nonlinear programming. New York: Springer.

Peto, R., & Peto, J. (1972). Asymptotically efficient rank invariant procedures. Journal of the Royal Statistical Society, 135, 185-207.

Phadke, M. S. (1989). Quality engineering using robust design. Englewood Cliffs, NJ: Prentice-Hall.

Phatak, A., Reilly, P. M., and Penlidis, A. (1993) An Approach to Interval Estimation in Partial Least Squares Regression, Analytica Chimica Acta, 277, 495-501

Piatetsky-Shapiro, G. (Ed.) (1993). Proceedings of AAAI-93 Workshop on Knowledge Discovery in Databases. Menlo Park, CA: AAAI Press.

Piepel, G. F. (1988). Programs for generating extreme vertices and centroids of linearly constrained experimental regions. Journal of Quality Technology, 20, 125-139.

Piepel, G. F., & Cornell, J. A. (1994). Mixture experiment approaches: Examples, discussion, and recommendations. Journal of Quality Technology, 26, 177-196.

Pigou, A. C. (1920). Economics of Welfare. London: Macmillan.

Pike, M. C. (1966). A method of analysis of certain class of experiments in carcinogenesis. Biometrics, 22, 142-161.

Pillai, K. C. S. (1965). On the distribution of the largest characteristic root of a matrix in multivariate analysis. Biometrika, 52, 405-414.

Plackett, R. L., & Burman, J. P. (1946). The design of optimum multifactorial experiments. Biometrika, 34, 255-272.

Polya, G. (1920). Uber den zentralen Grenzwertsatz der Wahrscheinlichkeitsrechnung und das Momentenproblem. Mathematische Zeitschrift, 8, 171-181.

Porebski, O. R. (1966). Discriminatory and canonical analysis of technical college data. British Journal of Mathematical and Statistical Psychology, 19, 215-236.

Powell, M. J. D. (1964). An efficient method for finding the minimum of a function of several variables without calculating derivatives. Computer Journal, 7, 155-162.

Pregibon, D. (1997). Data Mining. Statistical Computing and Graphics, 7, 8.

Prentice, R. (1973). Exponential survivals with censoring and explanatory variables. Biometrika, 60, 279-288.

Press, W. H., Flannery, B. P., Teukolsky, S. A., Vetterling, W. T. (1992). Numerical Recipes (2nd Edition). New York: Cambridge University Press.

Press, W.H., Teukolsky, S.A., Vetterling, W.T. and Flannery, B.P. (1992). Numerical Recipes in C: The Art of Scientific Computing (Second ed.). Cambridge University Press.

Press, William, H., Flannery, B. P., Teukolsky, S. A., Vetterling, W. T. (1986). Numerical Recipes. New York: Cambridge University Press.

Priestley, M. B. (1981). Spectral analysis and time series. New York: Academic Press.

Pyzdek, T. (1989). What every engineer should know about quality control. New York: Marcel Dekker.

Quinlan. (1992). C4.5: Programs for Machine Learning, Morgan Kaufmann

Quinlan, J.R., & Cameron-Jones, R.M. (1995). Oversearching and layered search in empirical learning. Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal (Vol. 2). Morgan Kaufman, 1019-10244.

Ralston, A., & Wilf, H.S. (Eds.). (1960). Mathematical methods for digital computers. New York: Wiley.

Ralston, A., & Wilf, H.S. (Eds.). (1967). Mathematical methods for digital computers (Vol. II). New York: Wiley.

Randles, R. H., & Wolfe, D. A. (1979). Introduction to the theory of nonparametric statistics. New York: Wiley.

Rannar, S., Lindgren, F., Geladi, P, and Wold, S. (1994) A PLS Kernel Algorithm for Data Sets with Many Variables and Fewer Objects. Part 1: Theory and Algorithm, Journal of Chemometrics, 8, 111-125.

Rao, C. R. (1951). An asymptotic expansion of the distribution of Wilks' criterion. Bulletin of the International Statistical Institute, 33, 177-181.

Rao, C. R. (1952). Advanced statistical methods in biometric research. New York: Wiley.

Rao, C. R. (1965). Linear statistical inference and its applications. New York: Wiley.

Rhoades, H. M., & Overall, J. E. (1982). A sample size correction for Pearson chi-square in 2 x 2 contingency tables. Psychological Bulletin, 91, 418-423.

Rinne, H., & Mittag, H. J. (1995). Statistische Methoden der Qualitätssicherung (3rd. edition). München/Wien: Hanser Verlag.

Ripley, B. D. (1981). Spacial statistics. New York: Wiley.

Ripley, B. D. (1996). Pattern recognition and neural networks. Cambridge: Cambridge University Press.

Ripley, B. D., (1996) Pattern Recognition and Neural Networks, Cambridge University Press

Rodriguez, R. N. (1992). Recent developments in process capability analysis. Journal of Quality Technology, 24, 176-187.

Rogan, J. C., Keselman, J. J., & Mendoza, J. L. (1979). Analysis of repeated measurements. British Journal of Mathematical and Statistical Psychology, 32, 269-286.

Rosenberg, S. (1977). New approaches to the analysis of personal constructs in person perception. In A. Landfield (Ed.), Nebraska symposium on motivation (Vol. 24). Lincoln, NE: University of Nebraska Press.

Rosenberg, S., & Sedlak, A. (1972). Structural representations of implicit personality theory. In L. Berkowitz (Ed.). Advances in experimental social psychology (Vol. 6). New York: Academic Press.

Rosenblatt, F. (1958). The Perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65, 386-408.

Roskam, E. E., & Lingoes, J. C. (1970). MINISSA-I: A Fortran IV program for the smallest space analysis of square symmetric matrices. Behavioral Science, 15, 204-205.

Ross, P. J. (1988). Taguchi techniques for quality engineering: Loss function, orthogonal experiments, parameter, and tolerance design. Milwaukee, Wisconsin: ASQC.

Roy, J. (1958). Step-down procedure in multivariate analysis. Annals of Mathematical Statistics, 29, 1177-1187.

Roy, J. (1967). Some aspects of multivariate analysis. New York: Wiley.

Roy, R. (1990). A primer on the Taguchi method. Milwaukee, Wisconsin: ASQC.

Royston, J. P. (1982). An extension of Shapiro and Wilks' W test for normality to large samples. Applied Statistics, 31, 115-124.

Rozeboom, W. W. (1979). Ridge regression: Bonanza or beguilement? Psychological Bulletin, 86, 242-249.

Rozeboom, W. W. (1988). Factor indeterminacy: the saga continues. British Journal of Mathematical and Statistical Psychology, 41, 209-226.

Rubinstein, L.V., Gail, M. H., & Santner, T. J. (1981). Planning the duration of a comparative clinical trial with loss to follow-up and a period of continued observation. Journal of Chronic Diseases, 34, 469479.

Rud, O., P. (2001). Data mining cookbook: Modeling data for marketing, risk, and customer relationship management. NY: Wiley.

Rumelhart, D.E. and McClelland, J. (eds.) (1986). Parallel Distributed Processing, Vol 1. Cambridge, MA: MIT Press.

Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986). Learning internal representations by error propagation. In D.E. Rumelhart, J.L. McClelland (Eds.), Parallel Distributed Processing, Vol 1. Cambridge, MA: MIT Press.

Runyon, R. P., & Haber, A. (1976). Fundamentals of behavioral statistics. Reading, MA: Addison-Wesley.

Ryan, T. P. (1989). Statistical methods for quality improvement. New York: Wiley.

Ryan, T. P. (1997). Modern Regression Methods. New York: Wiley.

Sandler, G. H. (1963). System reliability engineering. Englewood Cliffs, NJ: Prentice-Hall.

SAS Institute, Inc. (1982). SAS user's guide: Statistics, 1982 Edition. Cary, NC: SAS Institute, Inc.

Satorra, A., & Saris, W. E. (1985). Power of the likelihood ratio test in covariance structure analysis. Psychometrika, 50, 83-90.

Saxena, K. M. L., & Alam, K. (1982). Estimation of the noncentrality parameter of a chi squared distribution. Annals of Statistics, 10, 1012-1016.

Scheffé, H. (1953). A method for judging all possible contrasts in the analysis of variance. Biometrika, 40, 87-104.

Scheffé, H. (1959). The analysis of variance. New York: Wiley.

Scheffé, H. (1963). The simplex-centroid design for experiments with mixtures. Journal of the Royal Statistical Society, B25, 235-263.

Scheffé, H., & Tukey, J. W. (1944). A formula for sample sizes for population tolerance limits. Annals of Mathematical Statistics, 15, 217.

Scheines, R. (1994). Causation, indistinguishability, and regression. In F. Faulbaum, (Ed.), SoftStat '93. Advances in statistical software 4. Stuttgart: Gustav Fischer Verlag.

Schiffman, S. S., Reynolds, M. L., & Young, F. W. (1981). Introduction to multidimensional scaling: Theory, methods, and applications. New York: Academic Press.

Schmidt, F. L., & Hunter, J. E. (1997). Eight common but false objections to the discontinuation of significance testing in the analysis of research data. In Harlow, L. L., Mulaik, S. A., & Steiger, J. H. (Eds.), What if there were no significance tests. Mahwah, NJ: Lawrence Erlbaum Associates.

Schmidt, P., & Muller, E. N. (1978). The problem of multicollinearity in a multistage causal alienation model: A comparison of ordinary least squares, maximum-likelihood and ridge estimators. Quality and Quantity, 12, 267-297.

Schmidt, P., & Sickles, R. (1975). On the efficiency of the Almon lag technique. International Economic Review, 16, 792-795.

Schmidt, P., & Waud, R. N. (1973). The Almon lag technique and the monetary versus fiscal policy debate. Journal of the American Statistical Association, 68, 11-19.

Schnabel, R. B., Koontz, J. E., and Weiss, B. E. (1985). A modular system of algorithms for unconstrained minimization. ACM Transactions on Mathematical Software, 11, 419-440.

Schneider, H. (1986). Truncated and censored samples from normal distributions. New York: Marcel Dekker.

Schneider, H., & Barker, G.P. (1973). Matrices and linear algebra (2nd ed.). New York: Dover Publications.

Schönemann, P. H., & Steiger, J. H. (1976). Regression component analysis. British Journal of Mathematical and Statistical Psychology, 29, 175-189.

Schrock, E. M. (1957). Quality control and statistical methods. New York: Reinhold Publishing.

Schwarz, G. ( 1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464.

Scott, D. W. (1979). On optimal and data-based histograms. Biometrika, 66, 605-610.

Searle, S. R. (1987). Linear models for unbalanced data. New York: Wiley.

Searle, S. R., Casella, G., & McCulloch, C. E. (1992). Variance components. New York: Wiley.

Searle, S., R., Speed., F., M., & Milliken, G. A. (1980). The population marginal means in the linear model: An alternative to least squares means. The American Statistician, 34, 216-221.

Seber, G. A. F., & Wild, C. J. (1989). Nonlinear regression. New York: Wiley.

Sebestyen, G. S. (1962). Decision making processes in pattern recognition. New York: Macmillan.

Sen, P. K., & Puri, M. L. (1968). On a class of multivariate multisample rank order tests, II: Test for homogeneity of dispersion matrices. Sankhya, 30, 1-22.

Serlin, R. A., & Lapsley, D. K. (1993). Rational appraisal of psychological research and the good-enough principle. In G. Keren & C. Lewis (Eds.), A handbook for data analysis in the behavioral sciences: Methodological issues (pp. 199-228). Hillsdale, NJ: Lawrence Erlbaum Associates.

Serlin. R. A., & Lapsley, D. K. (1985). Rationality in psychological research: The good-enough principle. American Psychologist, 40, 7383.

Shapiro, A., & Browne, M. W. (1983). On the investigation of local identifiability: A counter example. Psychometrika, 48, 303-304.

Shapiro, S. S., Wilk, M. B., & Chen, H. J. (1968). A comparative study of various tests of normality. Journal of the American Statistical Association, 63, 1343-1372.

Shepherd, A. J. (1997). Second-Order Methods for Neural Networks. New York: Springer.

Shewhart, W. A. (1931). Economic control of quality of manufactured product. New York: D. Van Nostrand.

Shewhart, W. A. (1939). Statistical method from the viewpoint of quality. Washington, DC: The Graduate School Department of Agriculture.

Shirland, L. E. (1993). Statistical quality control with microcomputer applications. New York: Wiley.

Shiskin, J., Young, A. H., & Musgrave, J. C. (1967). The X-11 variant of the census method II seasonal adjustment program. (Technical paper no. 15). Bureau of the Census.

Shumway, R. H. (1988). Applied statistical time series analysis. Englewood Cliffs, NJ: Prentice Hall.

Siegel, A. E. (1956). Film-mediated fantasy aggression and strength of aggressive drive. Child Development, 27, 365-378.

Siegel, S. (1956). Nonparametric statistics for the behavioral sciences. New York: McGraw-Hill.

Siegel, S., & Castellan, N. J. (1988). Nonparametric statistics for the behavioral sciences (2nd ed.) New York: McGraw-Hill.

Simkin, D., & Hastie, R. (1986). Towards an information processing view of graph perception. Proceedings of the Section on Statistical Graphics, American Statistical Association, 11-20.

Sinha, S. K., & Kale, B. K. (1980). Life testing and reliability estimation. New York: Halstead.

Smirnov, N. V. (1948). Table for estimating the goodness of fit of empirical distributions. Annals of Mathematical Statistics, 19, 279-281.

Smith, D. J. (1972). Reliability engineering. New York: Barnes & Noble.

Smith, K. (1953). Distribution-free statistical methods and the concept of power efficiency. In L. Festinger and D. Katz (Eds.), Research methods in the behavioral sciences (pp. 536-577). New York: Dryden.

Sneath, P. H. A., & Sokal, R. R. (1973). Numerical taxonomy. San Francisco: W. H. Freeman & Co.

Snee, R. D. (1975). Experimental designs for quadratic models in constrained mixture spaces. Technometrics, 17, 149-159.

Snee, R. D. (1979). Experimental designs for mixture systems with multi-component constraints. Communications in Statistics - Theory and Methods, A8(4), 303-326.

Snee, R. D. (1985). Computer-aided design of experiments - some practical experiences. Journal of Quality Technology, 17, 222-236.

Snee, R. D. (1986). An alternative approach to fitting models when re-expression of the response is useful. Journal of Quality Technology, 18, 211-225.

Sokal, R. R., & Mitchener, C. D. (1958). A statistical method for evaluating systematic relationships. University of Kansas Science Bulletin, 38, 1409.

Sokal, R. R., & Sneath, P. H. A. (1963). Principles of numerical taxonomy. San Francisco: W. H. Freeman & Co.

Soper, H. E. (1914). Tables of Poisson's exponential binomial limit. Biometrika, 10, 25-35.

Spearman, C. (1904). "General intelligence," objectively determined and measured. American Journal of Psychology, 15, 201-293.

Speckt, D.F. (1990). Probabilistic Neural Networks. Neural Networks 3 (1), 109-118.

Speckt, D.F. (1991). A Generalized Regression Neural Network. IEEE Transactions on Neural Networks 2 (6), 568-576.

Spirtes, P., Glymour, C., & Scheines, R. (1993). Causation, prediction, and search. Lecture Notes in Statistics, V. 81. New York: Springer-Verlag.

Spjotvoll, E., & Stoline, M. R. (1973). An extension of the T-method of multiple comparison to include the cases with unequal sample sizes. Journal of the American Statistical Association, 68, 976-978.

Springer, M. D. (1979). The algebra of random variables. New York: Wiley.

Spruill, M. C. (1986). Computation of the maximum likelihood estimate of a noncentrality parameter. Journal of Multivariate Analysis, 18, 216-224.

Steiger, J. H. (1979). Factor indeterminacy in the 1930's and in the 1970's; some interesting parallels. Psychometrika, 44, 157-167.

Steiger, J. H. (1980a). Tests for comparing elements of a correlation matrix. Psychological Bulletin, 87, 245-251.

Steiger, J. H. (1980b). Testing pattern hypotheses on correlation matrices: Alternative statistics and some empirical results. Multivariate Behavioral Research, 15, 335-352.

Steiger, J. H. (1988). Aspects of person-machine communication in structural modeling of correlations and covariances. Multivariate Behavioral Research, 23, 281-290.

Steiger, J. H. (1989). EzPATH: A supplementary module for SYSTAT and SYGRAPH. Evanston, IL: SYSTAT, Inc.

Steiger, J. H. (1990). Some additional thoughts on components and factors. Multivariate Behavioral Research, 25, 41-45.

Steiger, J. H., & Browne, M. W. (1984). The comparison of interdependent correlations between optimal linear composites. Psychometrika, 49, 11-24.

Steiger, J. H., & Fouladi, R. T. (1992). R2: A computer program for interval estimation, power calculation, and hypothesis testing for the squared multiple correlation. Behavior Research Methods, Instruments, and Computers, 4, 581582.

Steiger, J. H., & Fouladi, R. T. (1997). Noncentrality interval estimation and the evaluation of statistical models. In Harlow, L. L., Mulaik, S. A., & Steiger, J. H. (Eds.), What if there were no significance tests. Mahwah, NJ: Lawrence Erlbaum Associates.

Steiger, J. H., & Hakstian, A. R. (1982). The asymptotic distribution of elements of a correlation matrix: Theory and application. British Journal of Mathematical and Statistical Psychology, 35, 208-215.

Steiger, J. H., & Lind, J. C. (1980). Statistically-based tests for the number of common factors. Paper presented at the annual Spring Meeting of the Psychometric Society in Iowa City. May 30, 1980.

Steiger, J. H., & Schönemann, P. H. (1978). A history of factor indeterminacy. In S. Shye, (Ed.), Theory Construction and Data Analysis in the Social Sciences. San Francisco: Jossey-Bass.

Steiger, J. H., Shapiro, A., & Browne, M. W. (1985). On the multivariate asymptotic distribution of sequential chi-square statistics. Psychometrika, 50, 253-264.

Stelzl, I. (1986). Changing causal relationships without changing the fit: Some rules for generating equivalent LISREL models. Multivariate Behavioral Research, 21, 309-331.

Stenger, F. (1973). Integration formula based on the trapezoid formula. Journal of the Institute of Mathematics and Applications, 12, 103-114.

Stevens, J. (1986). Applied multivariate statistics for the social sciences. Hillsdale, NJ: Erlbaum.

Stevens, W. L. (1939). Distribution of groups in a sequence of alternatives. Annals of Eugenics, 9, 10-17.

Stewart, D. K., & Love, W. A. (1968). A general canonical correlation index. Psychological Bulletin, 70, 160-163.

Steyer, R. (1992). Theorie causale regressionsmodelle [Theory of causal regression models]. Stuttgart: Gustav Fischer Verlag.

Steyer, R. (1994). Principles of causal modeling: a summary of its mathematical foundations and practical steps. In F. Faulbaum, (Ed.), SoftStat '93. Advances in statistical software 4. Stuttgart: Gustav Fischer Verlag.

Stone, M. and Brooks, R. J. (1990) Continuum Regression: Cross-validated Sequentially Constructed Prediction Embracing Ordinary Least Squares, Partial Least Squares, and Principal Components Regression, Journal of Royal Statistical Society, 52, No. 2, 237-269.

Student (1908). The probable error of a mean. Biometrika, 6, 1-25.

Swallow, W. H., & Monahan, J. F. (1984). Monte Carlo comparison of ANOVA, MIVQUE, REML, and ML estimators of variance components. Technometrics, 26, 47-57.

Taguchi, G. (1987). Jikken keikakuho (3rd ed., Vol I & II). Tokyo: Maruzen. English translation edited by D. Clausing. System of experimental design. New York: UNIPUB/Kraus International

Taguchi, G., & Jugulum, R. (2002). The Mahalanobis-Taguchi strategy. New York, NY: Wiley.

Tanaka, J. S., & Huba, G. J. (1985). A fit index for covariance structure models under arbitrary GLS estimation. British Journal of Mathematical and Statistical Psychology, 38, 197-201.

Tanaka, J. S., & Huba, G. J. (1989). A general coefficient of determination for covariance structure models under arbitrary GLS estimation. British Journal of Mathematical and Statistical Psychology, 42, 233-239.

Tatsuoka, M. M. (1970). Discriminant analysis. Champaign, IL: Institute for Personality and Ability Testing.

Tatsuoka, M. M. (1971). Multivariate analysis. New York: Wiley.

Tatsuoka, M. M. (1976). Discriminant analysis. In P. M. Bentler, D. J. Lettieri, and G. A. Austin (Eds.), Data analysis strategies and designs for substance abuse research. Washington, DC: U.S. Government Printing Office.

Taylor, D. J., & Muller, K. E. (1995). Computing confidence bounds for power and sample size of the general linear univariate model. The American Statistician, 49, 4347.

Thorndike, R. L., & Hagen, E. P. (1977). Measurement and evaluation in psychology and education. New York: Wiley.

Thurstone, L. L. (1931). Multiple factor analysis. Psychological Review, 38, 406-427.

Thurstone, L. L. (1947). Multiple factor analysis. Chicago: University of Chicago Press.

Timm, N. H. (1975). Multivariate analysis with applications in education and psychology. Monterey, CA: Brooks/Cole.

Timm, N. H., & Carlson, J. (1973). Multivariate analysis of non-orthogonal experimental designs using a multivariate full rank model. Paper presented at the American Statistical Association Meeting, New York.

Timm, N. H., & Carlson, J. (1975). Analysis of variance through full rank models. Multivariate behavioral research monographs, No. 75-1.

Tracey, N. D., Young, J., C., & Mason, R. L. (1992). Multivariate control charts for individual observations. Journal of Quality Technology, 2, 88-95.

Tribus, M., & Szonyi, G. (1989). An alternative view of the Taguchi approach. Quality Progress, 22, 46-48.

Trivedi, P. K., & Pagan, A. R. (1979). Polynomial distributed lags: A unified treatment. Economic Studies Quarterly, 30, 37-49.

Tryon, R. C. (1939). Cluster Analysis. Ann Arbor, MI: Edwards Brothers.

Tucker, L. R., Koopman, R. F., & Linn, R. L. (1969). Evaluation of factor analytic research procedures by means of simulated correlation matrices. Psychometrika, 34, 421-459.

Tufte, E. R. (1983). The visual display of quantitative information. Cheshire, CT: Graphics Press.

Tufte, E. R. (1990). Envisioning information. Cheshire, CT: Graphics Press.

Tukey, J. W. (1953). The problem of multiple comparisons. Unpublished manuscript, Princeton University.

Tukey, J. W. (1962). The future of data analysis. Annals of Mathematical Statistics, 33, 1-67.

Tukey, J. W. (1967). An introduction to the calculations of numerical spectrum analysis. In B. Harris (Ed.), Spectral analysis of time series. New York: Wiley.

Tukey, J. W. (1972). Some graphic and semigraphic displays. In Statistical Papers in Honor of George W. Snedecor; ed. T. A. Bancroft, Arnes, IA: Iowa State University Press, 293-316.

Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley.

Tukey, J. W. (1984). The collected works of John W. Tukey. Monterey, CA: Wadsworth.

Tukey, P. A. (1986). A data analyst's view of statistical plots. Proceedings of the Section on Statistical Graphics, American Statistical Association, 21-28.

Tukey, P. A., & Tukey, J. W. (1981). Graphical display of data sets in 3 or more dimensions. In V. Barnett (Ed.), Interpreting multivariate data. Chichester, U.K.: Wiley.

Uspensky, J. V. (1937). Introduction to Mathematical Probability. New York: McGraw-Hill.

Vale, C. D., & Maurelli, V. A. (1983). Simulating multivariate non-normal distributions. Psychometrika, 48, 465-471.

Vandaele, W. (1983). Applied time series and Box-Jenkins models. New York: Academic Press.

Vaughn, R. C. (1974). Quality control. Ames, IA: Iowa State Press.

Velicer, W. F., & Jackson, D. N. (1990). Component analysis vs. factor analysis: some issues in selecting an appropriate procedure. Multivariate Behavioral Research, 25, 1-28.

Velleman, P. F., & Hoaglin, D. C. (1981). Applications, basics, and computing of exploratory data analysis. Belmont, CA: Duxbury Press.

Von Mises, R. (1941). Grundlagen der Wahrscheinlichkeitsrechnung. Mathematische Zeitschrift, 5, 52-99.

Wainer, H. (1995). Visual revelations. Chance, 8, 48-54.

Wald, A. (1939). Contributions to the theory of statistical estimation and testing hypotheses. Annals of Mathematical Statistics, 10, 299-326.

Wald, A. (1945). Sequential tests of statistical hypotheses. Annals of Mathematical Statistics, 16, 117-186.

Wald, A. (1947). Sequential analysis. New York: Wiley.

Walker, J. S. (1991). Fast Fourier transforms. Boca Raton, FL: CRC Press.

Wallis, K. F. (1974). Seasonal adjustment and relations between variables. Journal of the American Statistical Association, 69, 18-31.

Wang, C. M., & Gugel, H. W. (1986). High-performance graphics for exploring multivariate data. Proceedings of the Section on Statistical Graphics, American Statistical Association, 60-65.

Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236.

Warner B. & Misra, M. (1996). Understanding Neural Networks as Statistical Tools. The American Statistician, 50, 284-293.

Weatherburn, C. E. (1946). A First Course in Mathematical Statistics. Cambridge: Cambridge University Press.

Wei, W. W. (1989). Time series analysis: Univariate and multivariate methods. New York: Addison-Wesley.

Weibull, W. (1951). A statistical distribution function of wide applicability. Journal of Applied Mechanics, September.

Weibull, W., (1939). A statistical theory of the strength of materials. Ing. Velenskaps Akad. Handl., 151, 1-45.

Weigend, A.S., Rumelhart, D.E. and Huberman, B.A. (1991). Generalization by weight-elimination with application to forecasting. In R.P. Lippmann, J.E. Moody and D.S. Touretzky (Eds.) Advances in Neural Information Processing Systems 3, 875-882. San Mateo, CA: Morgan Kaufmann.

Weiss, S. M., & Indurkhya, N. (1997). Predictive data mining: A practical guide. New York: Morgan-Kaufman.

Welch, B. L. (1938). The significance of the differences between two means when the population variances are unequal. Biometrika, 29, 350-362.

Welstead, S. T. (1994). Neural network and fuzzy logic applications in C/C++. New York: Wiley.

Werbos, P.J. (1974). Beyond regression: new tools for prediction and analysis in the behavioural sciences. Ph.D. thesis, Harvard University, Boston, MA.

Wescott, M. E. (1947). Attribute charts in quality control. Conference Papers, First Annual Convention of the American Society for Quality Control. Chicago: John S. Swift Co.

Westphal, C., Blaxton, T. (1998). Data mining solutions. New York: Wiley.

Wheaton, B., Múthen, B., Alwin, D., & Summers G. (1977). Assessing reliability and stability in panel models. In D. R. Heise (Ed.), Sociological Methodology. New York: Wiley.

Wheeler, D. J., & Chambers, D.S. (1986). Understanding statistical process control. Knoxville, TN: Statistical Process Controls, Inc.

Wherry, R. J. (1984). Contributions to correlational analysis. New York: Academic Press.

Whitney, D. R. (1948). A comparison of the power of non-parametric tests and tests based on the normal distribution under non-normal alternatives. Unpublished doctoral dissertation, Ohio State University.

Whitney, D. R. (1951). A bivariate extension of the U statistic. Annals of Mathematical Statistics, 22, 274-282.

Widrow, B., and Hoff Jr., M.E. (1960). Adaptive switching circuits. IRE WESCON Convention Record, 96-104.

Wiggins, J. S., Steiger, J. H., and Gaelick, L. (1981). Evaluating circumplexity in models of personality. Multivariate Behavioral Research, 16, 263-289.

Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1, 80-83.

Wilcoxon, F. (1947). Probability tables for individual comparisons by ranking methods. Biometrics, 3, 119-122.

Wilcoxon, F. (1949). Some rapid approximate statistical procedures. Stamford, CT: American Cyanamid Co.

Wilde, D. J., & Beightler, C. S. (1967). Foundations of optimization. Englewood Cliffs, NJ: Prentice-Hall.

Wilks, S. S. (1943). Mathematical Statistics. Princeton, NJ: Princeton University Press.

Wilks, S. S. (1946). Mathematical statistics. Princeton, NJ: Princeton University Press.

Williams, W. T., Lance, G. N., Dale, M. B., & Clifford, H. T. (1971). Controversy concerning the criteria for taxonometric strategies. Computer Journal, 14, 162.

Wilson, E. B., & Hilferty, M. M. (1931). The distribution of chi-square. Proceedings of the National Academy of Sciences of the United States of America, 17, 684-688.

Wilson, G. A., & Martin, S. A. (1983). An empirical comparison of two methods of testing the significance of a correlation matrix. Educational and Psychological Measurement, 43, 11-14.

Winer, B. J. (1962). Statistical principles in experimental design. New York: McGraw-Hill.

Winer, B. J. (1971). Statistical principles in experimental design (2nd ed.). New York: McGraw Hill.

Winer, B. J., Brown, D. R., Michels, K. M. (1991). Statistical principals in experimental design. (3rd ed.). New York: McGraw-Hill.

Witten, I., H., & Frank, E. (2000). Data Mining: Practical Machine Learning Tools and Techniques. New York: Morgan Kaufmann.

Wolfowitz, J. (1942). Additive partition functions and a class of statistical hypotheses. Annals of Mathematical Statistics, 13, 247-279.

Wolynetz, M. S. (1979a). Maximum likelihood estimation from confined and censored normal data. Applied Statistics, 28, 185-195.

Wolynetz, M. S. (1979b). Maximum likelihood estimation in a linear model from confined and censored normal data. Applied Statistics, 28, 195-206.

Wonnacott, R. J., & Wonnacott, T. H. (1970). Econometrics. New York: Wiley.

Woodward, J. A., & Overall, J. E. (1975). Multivariate analysis of variance by multiple regression methods. Psychological Bulletin, 82, 21-32.

Woodward, J. A., & Overall, J. E. (1976). Calculation of power of the F test. Educational and Psychological Measurement, 36, 165-168.

Woodward, J. A., Bonett, D. G., & Brecht, M. L. (1990). Introduction to linear models and experimental design. New York: Harcourt, Brace, Jovanovich.

Yates, F. (1933). The principles of orthogonality and confounding in replicated experiments. Journal of Agricultural Science, 23, 108-145.

Yates, F. (1937). The Design and Analysis of Factorial Experiments. Imperial Bureau of Soil Science, Technical Communication No. 35, Harpenden.

Yokoyama, Y., & Taguchi, G. (1975). Business data analysis: Experimental regression analysis. Tokyo: Maruzen.

Youden, W. J., & Zimmerman, P. W. (1936). Field trials with fiber pots. Contributions from Boyce Thompson Institute, 8, 317-331.

Young, F. W, & Hamer, R. M. (1987). Multidimensional scaling: History, theory, and applications. Hillsdale, NJ: Erlbaum

Young, F. W., Kent, D. P., & Kuhfeld, W. F. (1986). Visuals: Software for dynamic hyper-dimensional graphics. Proceedings of the Section on Statistical Graphics, American Statistical Association, 69-74.

Younger, M. S. (1985). A first course in linear regression (2nd ed.). Boston: Duxbury Press.

Yuen, C. K., & Fraser, D. (1979). Digital spectral analysis. Melbourne: CSIRO/Pitman.

Yule, G. U. (1897). On the theory of correlation. Journal of the Royal Statistical Society, 60, 812-854.

Yule, G. U. (1907). On the theory of correlation for any number of variables treated by a new system of notation. Proceedings of the Royal Society, Ser. A, 79, 182-193.

Yule, G. U. (1911). An Introduction to the Theory of Statistics. London: Griffin.

Zippin, C., & Armitage, P. (1966). Use of concomitant variables and incomplete survival information in the estimation of an exponential survival parameter. Biometrics, 22, 665-672.

Zupan, J. (1982). Clustering of large data sets. New York: Research Studies Press.

Zweig, M.H., & Campbell, G. (1993). Receiver-Operating Characteristic (ROC) Plots: A Fundamental Evaluation Tool in Clinical Medicine. Clin. Chem 39 (4), pp. 561-577.

Zwick, W. R., & Velicer, W. F. (1986). Comparison of five rules for determining the number of components to retain. Psychological Bulletin, 99, 432-442.