Agresti, A. (2013). Categorical Data Analysis. 3rd ed. Hoboken, NJ: John Wiley & Sons.
Bartlett, M. S. (1937). “Properties of sufficiency and statistical tests.” Proceedings of the Royal Society of London, Series A 160:268–282.
Borg, I., and Groenen, P. J. F. (2005). Modern Multidimensional Scaling: Theory and Applications. 2nd ed. New York: Springer.
Collins, L., and Lanza, S. (2010). Latent Class and Latent Transition Analysis. Hoboken NJ: John Wiley & Sons.
Cox, I., and Gaudard, M. (2013). Discovering Partial Least Squares with JMP. Cary, NC: SAS Institute Inc.
de Ayala, R. J. (2009). The Theory and Practice of Item Response Theory. New York: Guilford Press.
Eriksson, L., Johansson, E., Kettaneh-Wold, N., Trygg, J., Wikstrom, C., and Wold, S. (2006). Multi- and Megavariate Data Analysis Basic Principles and Applications (Part I). Chapter 4. Umetrics.
Frank, I. E., and Todeschini, T. (1994). The Data Analysis Handbook. New York: Elsevier.
Friedman, J. H. (1989). “Regularized Discriminant Analysis.” Journal of the American Statistical Association 84:165–175.
Garthwaite, P. (1994). “An Interpretation of Partial Least Squares.” Journal of the American Statistical Association 89:122–127.
Golub, G. H., and Kahan, W. (1965). “Calculating the singular values and pseudo-inverse of a matrix.” Journal of the Society for Industrial and Applied Mathematics: Series B, Numerical Analysis 2:205–224.
Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis. London: Academic Press.
Hand, D., Mannila, H., and Smyth, P. (2001). Principles of Data Mining. Cambridge, MA: MIT Press.
Hartigan, J. A. (1981). “Consistency of Single Linkage for High–Density Clusters.” Journal of the American Statistical Association 76:388–394.
Hastie, T., Tibshirani, R., and Friedman, J. H.(2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer Verlag.
Hoskuldsson, A. (1988). “PLS Regression Methods.” Journal of Chemometrics 2:211–228.
Hoeffding, W. (1948). “A Non-Parametric Test of Independence.” Annals of Mathematical Statistics 19:546–557.
Huber, P. J., and Ronchetti, E. M. (2009). Robust Statistics. 2nd ed. Hoboken, NJ: John Wiley & Sons.
Jackson, J. E. (2003). A User’s Guide to Principal Components. Hoboken, NJ: John Wiley & Sons.
Jardine, N., and Sibson, R. (1971). Mathematical Taxonomy. New York: John Wiley & Sons.
Jöreskog, K. G. (1977). “Factor Analysis by Least-Squares and Maximum Likelihood Methods.” In Statistical Methods for Digital Computers, edited by K. Enslein, A. Ralston, and H. Wilf, 125 - 165. New York: John Wiley & Sons.
Kohonen, T. (1989). Self-Organization and Associative Memory. 3rd ed. Vol. 8 of Springer Series in Information. Berlin: Springer-Verlag.
Kohonen, T. (1990). “The Self-Organizing Map.” Proceedings of the IEEE 78:1464–1480.
Mardia, K., Kent, J., and Bibby, J. (1980). Multivariate Analysis. New York: Academic Press.
Mason, R. L., and Young, J. C. (2002). Multivariate Statistical Process Control with Industrial Applications. Philadelphia: SIAM.
McLachlan, G. J., and Krishnan, T. (1997). The EM Algorithm and Extensions. New York: John Wiley & Sons.
Nunnally, J. C. (1978). Psychometric theory. 2nd ed. New York: McGraw-Hill.
Press, W. H, Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P. (1998). Numerical Recipes in C: The Art of Scientific Computing. 2nd ed. Cambridge, England: Cambridge University Press.
Rasch, G. (1980). Probabilistic Models for Some Intelligence and Attainment Tests. Chicago: University of Chicago Press.
SAS Institute Inc. (1983). SAS Technical Report A-108: Cubic Clustering Criterion. Cary, NC: SAS Institute Inc. Retrieved December 16, 2015 from https://support.sas.com/documentation/onlinedoc/v82/techreport_a108.pdf
SAS Institute Inc. (2017a). “The CANDISC Procedure.” SAS/STAT 14.3 User’s Guide. Cary, NC: SAS Institute Inc. http://support.sas.com/documentation/onlinedoc/stat/143/candisc.pdf
SAS Institute Inc. (2017b). “The FACTOR Procedure.” SAS/STAT 14.3 User’s Guide. Cary, NC: SAS Institute Inc. http://support.sas.com/documentation/onlinedoc/stat/143/factor.pdf
SAS Institute Inc. (2017c). “The FASTCLUS Procedure.” SAS/STAT 14.3 User’s Guide. Cary, NC: SAS Institute Inc. http://support.sas.com/documentation/onlinedoc/stat/143/fastclus.pdf
SAS Institute Inc. (2017d). “The PLS Procedure.” SAS/STAT 14.3 User’s Guide. Cary, NC: SAS Institute Inc. http://support.sas.com/documentation/onlinedoc/stat/143/pls.pdf
SAS Institute Inc. (2017e). “The VARCLUS Procedure.” SAS/STAT 14.3 User’s Guide. Cary, NC: SAS Institute Inc. http://support.sas.com/documentation/onlinedoc/stat/143/varclus.pdf
Tobias, R. D. (1995). “An Introduction to Partial Least Squares Regression.” In Proceedings of the Twentieth Annual SAS Users Group International Conference, 1250–1257. Cary, NC: SAS Institute Inc. http://www.sascommunity.org/sugi/SUGI95/Sugi-95-210%20Tobias.pdf
Umetrics. (1995). Multivariate Analysis (3-day course). Winchester, MA.
Wold, S. (1994). “PLS for Multivariate Linear Modeling.” In QSAR: Chemometric Methods in Molecular Design. Methods and Principles in Medicinal Chemistry, edited by H. van de Waterbeemd, pp. 195–218. Weinheim, Germany: Verlag-Chemie.

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