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

Marden, John I

ISBN 10: 1456538837 / ISBN 13: 9781456538835

Editore: CreateSpace Independent Publishing Platform, 2015

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Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing and/or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes). Codice articolo 002273355U

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Titolo: Multivariate Statistics: Old School: ...

Casa editrice: CreateSpace Independent Publishing Platform

Data di pubblicazione: 2015

Legatura: Paperback

Condizione: Good

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Riassunto:

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.