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Multivariate Statistics: Old School: Mathematical and methodological introduction to multivariate statistical analytics, including linear models, ... machine learning and big data study, with R

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Multivariate Statistics: Old School is a mathematical and methodological introduction to multivariate statistical analysis. It presents the basic mathematical grounding that graduate statistics students need for future research, and important multivariate techniques useful to statisticians in general. The material provides support for further study in big data and machine learning. Topics include The multivariate normal and Wishart distributions Linear models, including multivariate regression and analysis of variance, and both-sides models (GMANOVA, repeated measures, growth curves) Linear algebra useful for multivariate statistics Covariance structures, including principal components, factor analysis, independence and conditional independence, and symmetry models Classification (linear and quadratic discrimination, trees, logistic regression) Clustering (K-means, model-based, hierarchical) Other techniques, including biplots, canonical correlations, and multidimensional scaling Most of the analyses in the book use the statistical computing environment R, for which there is an available package (msos) of multivariate routines and data sets. This text was developed over many years by the author, John Marden, while teaching in the Department of Statistics, University of Illinois at Urbana-Champaign.

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Multivariate Statistics: Old School: Mathematical and methodological introduction to multivariate statistical analytics, including linear models, ... machine learning and big data study, with R

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Multivariate Statistics: Old School: Mathematical and methodological introduction to multivariate statistical analytics, including linear models, ... machine learning and big data study, with R

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