The following sources are referenced in Multivariate Methods.
Agresti, A. (2013). Categorical Data Analysis. 3rd ed. Hoboken, NJ: John Wiley & Sons.
Baglama, J., and Reichel, L. (2005). “Augmented implicitly restarted Lanczos bidiagonalization methods.” SIAM Journal on Scientific Computing 27:19–42.
Ballard, D. H. (1981). “Generalizing the Hough Transform to Detect Arbitrary Shapes.” Pattern Recognition 13:111–122.
Bartlett, M. S. (1937). “Properties of sufficiency and statistical tests.” Proceedings of the Royal Society of London, Series A 160:268–282.
Bartlett, M. S. (1954). “A Note on the Multiplying Factors for Various Chi Square Approximations.” Journal of the Royal Statistical Society, Series B 16:296–298.
Benzécri, J. P. (1979). “Sur le calcul des taux d’inertie dans l’analyse d’un questionnaire, addendum et erratum à [BIN. MULT.].” Cahiers de l’Analyse des Données 4:377–378.
Bentler, P. M. (1990). “Comparative Fit Indexes in Structural Models.” Psychological Bulletin 107:238.
Bentler, P. M., and Freeman, E. H. (1983). “Tests for Stability in Linear Structural Equation Systems.” Psychometrika 48:143–145.
Bernhardsson, E. (2013). “Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk.” https://github.com/spotify/annoy
Bollen, K. A. (1989). Structural Equations with Latent Variables. New York: John Wiley & Sons.
Bollen, K. A., Harden, J. J., Ray, S., and Zavisca, J. (2014). “BIC and Alternative Bayesian Information Criteria in the Selection of Structural Equation Models.” Structural Equation Modeling 21:1–19.
Borg, I., and Groenen, P. J. F. (2005). Modern Multidimensional Scaling: Theory and Applications. 2nd ed. New York: Springer.
Boulesteix, A.-L., and Strimmer, K. (2007). “Partial Least Squares: A Versatile Tool for the Analysis of High-Dimensional Genomic Data.” Briefings in Bioinformatics 8:32–44.
Browne, M. (2001). “An Overview of Analytic Rotation in Exploratory Factor Analysis.” Multivariate Behavioral Research 36:111–150.
Browne, M. W., and Cudeck, R. (1993). “Alternative Ways of Assessing Model Fit.” In Testing Structural Equation Models, edited by K. A. Bollen, and J. S. Long, 136–162. Newbury Park, CA: Sage Publications.
Chen, F. F. (2007). “Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance.” Structural Equation Modeling 14:464–504.
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.
Cronbach, L. J. (1951). “Coefficient Alpha and the Internal Structure of Tests.” Psychometrika 16:297–334.
Cudeck, R., and MacCallum, R. C., eds. (2007). Factor Analysis at 100, Historical Developments and Future Directions. Mahwah, NJ: Lawrence Erlbaum Associates.
de Ayala, R. J. (2009). The Theory and Practice of Item Response Theory. New York: Guilford Press.
De Jong, S. (1993). “SIMPLS: An Alternative Approach to Partial Least Squares Regression.” Chemometrics and Intelligent Laboratory Systems 18:251–263.
Denham, M. C. (1997). “Prediction Intervals in Partial Least Squares.” Journal of Chemometrics 11:39–52.
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.
Finkbeiner, C. (1979). “Estimation for the Multiple Factor Model when Data are Missing.” Psychometrika 44:409–420.
Fisher, L., and Van Ness, J. W. (1971). “Admissible Clustering Procedures.” Biometrika 58:91–104.
Florek, K., Lukaszewicz, J., Perkal, J., and Zubrzycki, S. (1951a). “Sur la liaison et la division des points d’un ensemble fini.” Colloquium Mathematicae 2:282–285.
Florek, K., Lukaszewicz, J., Perkal, J., and Zubrzycki, S. (1951b). “Taksonomia Wroclawska.” Przeglad Antropologiczny 17:193–211.
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.
Goodman, L. A. (1974). “Exploratory Latent Structure Analysis Using Both Identifiable and Unidentifiable Models.” Biometrika 61:215–231.
Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis. London: Academic Press.
Halko, N., Martinsson, P. G., & Tropp, J. A. (2011). “Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions.” SIAM Review 53:217–288.
Hancock, G. R., and Mueller, R. O. (2001). “Rethinking Construct Reliability within Latent Variable Systems.” In Structural Equation Modeling: Present and Future – A Festschrift in Honor of Karl Jöreskog, edited by R. Cudeck, S. du Toit, and D. Sörbom, 195–216. Lincolnwood, IL: Scientific Software International.
Hand, D., Mannila, H., and Smyth, P. (2001). Principles of Data Mining. Cambridge, MA: MIT Press.
Harris, C. W., and Kaiser, H. F. (1964). “Oblique Factor Analytic Solutions by Orthogonal Transformation.” Psychometrika 32:363–379.
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.
Hinton, G. E., and Roweis, S. T. (2002). “Stochastic Neighbor Embedding.” Advances in Neural Information Processing Systems 15:833–840.
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.
Hu, L.-T., and Bentler, P. M. (1999). “Cutoff Criteria for Fit Indices in Covariance Structure Analysis: Conventional Criteria versus New Alternatives.” Structural Equation Modeling 6:1–55.
Huber, P. J. (1964). “Robust Estimation of a Location Parameter.” Annals of Mathematical Statistics 35:73–101.
Huber, P. J. (1973). “Robust Regression: Asymptotics, Conjecture, and Monte Carlo.” Annals of Statistics 1:799–821.
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.
Kingma, D. P., and Ba, J. (2014). “Adam: A method for stochastic optimization.” arXiv preprint arXiv:1412.6980.
Kline, R. B. (2016). Principles and Practice of Structural Equation Methodology. 4th ed. New York: The Guilford Press.
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.
Lindberg, W., Persson, J.-A., and Wold, S. (1983). “Partial Least-Squares Method for Spectrofluorimetric Analysis of Mixtures of Humic Acid and Ligninsulfonate.” Analytical Chemistry 55:643–648.
LeRoux, B., and Rouanet, H. (2010). Multiple Correspondence Analysis. Vol.07–163 of Sage University Paper Series on Quantitative Applications in the Social Sciences. Thousand Oaks, CA: Sage Publications.
Maiti, S. S., and Mukherjee, B. N. (1991). “Two New Goodness-of-Fit Indices for Covariance Matrices with Linear Structures.” British Journal of Mathematical and Statistical Psychology 44:153–180.
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.
May, J. P. (1992). Simplicial Objects in Algebraic Topology. Vol. 11. Chicago: University of Chicago Press.
Maydeu-Olivares, A., Shi, D., and Rosseel, Y. (2017). “Assessing Fit in Structural Equation Models: A Monte-Carlo Evaluation of RMSEA Versus SRMR Confidence Intervals and Tests of Close Fit.” Structural Equation Model: A Multidisciplinary Journal 25:389–402.
McDonald, R. P. (1999). Test Theory: A Unified Approach. Mahwah, NJ: Erlbaum.
McDonald, R. P., and Marsh, H. W. (1990). “Choosing a Multivariate Model: Noncentrality and Goodness of Fit.” Psychological Bulletin 107:247–255.
McInnes, L., Healy, J., and Melville, J. (2018). “UMAP: Uniform manifold approximation and projection for dimension reduction.” arXiv preprint arXiv:1802.03426.
McLachlan, G. J., and Krishnan, T. (1997). The EM Algorithm and Extensions. New York: John Wiley & Sons.
McQuitty, L. L. (1957). “Elementary Linkage Analysis for Isolating Orthogonal and Oblique Types and Typal Relevancies.” Educational and Psychological Measurement 17:207–229.
Milligan, G. W. (1980). “An Examination of the Effect of Six Types of Error Perturbation on Fifteen Clustering Algorithms.” Psychometrika 45:325–342.
Nelson, P. R. C., Taylor, P. A., and MacGregor, J. F. (1996). “Missing Data Methods in PCA and PLS: Score calculations with incomplete observations.” Chemometrics and Intelligent Laboratory Systems 35:45–65.
Nunnally, J. C. (1978). Psychometric theory. 2nd ed. New York: McGraw-Hill.
Penny, K. I. (1996). “Appropriate Critical Values When Testing for a Single Multivariate Outlier by Using the Mahalanobis Distance.” Journal of the Royal Statistical Society, Series C 45:73–81.
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.
Rodriguez, A., Reise, S. P., and Haviland, M. G. (2016). “Evaluating Bifactor Models: Calculating and Interpreting Statistical Indices. Psychological Methods 21:137.
Saad, D. (1998). “Online algorithms and stochastic approximations.” Online Learning 5(3):6.
SAS Institute Inc. (1983). SAS Technical Report A-108: Cubic Clustering Criterion. Cary, NC: SAS Institute Inc. https://support.sas.com/kb/22/addl/fusion_22540_1_a108_5903.pdf.
SAS Institute Inc. (2022a). “The CALIS Procedure.” SAS/STAT® User’s Guide. Cary, NC: SAS Institute Inc. https://go.documentation.sas.com/api/collections/pgmsascdc/v_034/docsets/statug/content/calis.pdf.
SAS Institute Inc. (2022b). “The CANDISC Procedure.” SAS/STAT® User’s Guide. Cary, NC: SAS Institute Inc. https://go.documentation.sas.com/api/collections/pgmsascdc/v_034/docsets/statug/content/candisc.pdf.
SAS Institute Inc. (2022c). “The FACTOR Procedure.” SAS/STAT® User’s Guide. Cary, NC: SAS Institute Inc. https://go.documentation.sas.com/api/collections/pgmsascdc/v_034/docsets/statug/content/factor.pdf.
SAS Institute Inc. (2022d). “The FASTCLUS Procedure.” SAS/STAT® User’s Guide. Cary, NC: SAS Institute Inc. https://go.documentation.sas.com/api/collections/pgmsascdc/v_034/docsets/statug/content/fastclus.pdf.
SAS Institute Inc. (2022e). “The MIXED Procedure.” SAS/STAT® User’s Guide. Cary, NC: SAS Institute Inc. https://go.documentation.sas.com/api/collections/pgmsascdc/v_034/docsets/statug/content/mixed.pdf.
SAS Institute Inc. (2022f). “The PLS Procedure.” SAS/STAT® User’s Guide. Cary, NC: SAS Institute Inc. https://go.documentation.sas.com/api/collections/pgmsascdc/v_034/docsets/statug/content/pls.pdf.
SAS Institute Inc. (2022g). “The VARCLUS Procedure.” SAS/STAT® User’s Guide. Cary, NC: SAS Institute Inc. https://go.documentation.sas.com/api/collections/pgmsascdc/v_034/docsets/statug/content/varclus.pdf.
Schafer, J., and Strimmer, K. (2005). “A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics.” Statistical Applications in Genetics and Molecular Biology 4 Article 32.
Sneath, P. H. A. (1957). “The Application of Computers to Taxonomy.” Journal of General Microbiology 17:201–226.
Sokal, R. R., and Michener, C. D. (1958). “A Statistical Method for Evaluating Systematic Relationships.” University of Kansas Science Bulletin 38:1409–1438.
Steiger, J. H. (1989). EzPATH: A Supplementary Module for SYSTAT and SYGRAPH. Evanston, IL: Systat.
Steiger, J. H. (1990). “Structural Model Evaluation and Modification: An Interval Estimation Approach.” Multivariate Behavioral Research 25:173–180.
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.
Tracy, N. D., Young, J. C., and Mason, R. R. (1992). “Multivariate Control Charts for Individual Observations.” Journal of Quality Technology 24:88–95.
Umetrics. (1995). Multivariate Analysis (3-day course). Winchester, MA.
Van der Maaten, L. (2014). “Accelerating t-SNE using tree-based algorithms.” The Journal of Machine Learning Research 15:3221–3245.
Van der Maaten, L., and Hinton, G. E. (2008). “Visualizing Data using t-SNE.” Journal of Machine Learning Research 9(11).
Waern, Y. (1972). “Structure in Similarity Matrices: A Graphic Approach.” Scandinavian Journal of Psychology 13:5–16.
West, S. G., Taylor, A. B., and Wu, W. (2012). “Model Fit and Model Selection in Structural Equation Modeling.” In Handbook of Structural Equation Modeling, edited by R. H. Hoyle, 209–231. New York: The Guilford Press.
White, K. P., Jr., Kundu, B., and Mastrangelo, C. M. (2008). “Classification of Defect Clusters on Semiconductor Wafers Via the Hough Transform.” IEEE Transactions on Semiconductor Manufacturing 21:272–278.
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.
Wold, S., Sjostrom, M., and Eriksson, L. (2001). “PLS-Regression: A Basic Tool of Chemometrics.” Chemometrics and Intelligent Laboratory Systems 58:109–130.