Multivariate Statistics: Old School: Mathematical and methodological introduction to multivariate statistical analytics, including linear model

Name: Multivariate Statistics: Old School: Mathematical and methodological introduction to multivariate statistical analytics, including linear model
Brand: Createspace Independent Publishing Platform
SKU: 9781456538835
Price: 2657.0 INR
Availability: InStock

by John I. Marden

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ISBN13: 9781456538835
Binding: Paperback
Subject: N/A
Publisher: Createspace Independent Publishing Platform
Publisher Imprint: Createspace Independent Publishing Platform
Publication Date: September 14, 2015
Pages: 360
Original Price: USD 29.95
Language: English
Edition: N/A
Item Weight: 481 grams
BISAC Subject(s): Probability & Statistics / Multivariate Analysis

About The Book

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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