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

Editor

CreateSpace Independent Publishing Platform

Fecha de publicaci�n

2015

Idioma

Ingl�s

ISBN 10

1456538837

ISBN 13

9781456538835

Encuadernaci�n

Encuadernaci�n de tapa blanda

Estado

New

Cat�logos de vendedores

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Sinopsis

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.

Rese�a del editor

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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J2B Holdings, LLC
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