Description: Applied Multivariate Analysis : Using Bayesian And Frequentist Methods Of Inference, Paperback by Press, S. James, ISBN 0486442365, ISBN-13 9780486442365, Like New Used, Free shipping in the US Geared toward upper-level undergraduates and graduate students, this two-part treatment deals with the foundations of multivariate analysis as well as related models and applications. Starting with a look at practical elements of matrix theory, the text proceeds to discussions of continuous multivariate distributions, the normal distribution, and Bayesian inference; multivariate large sample distributions and approximations; the Wishart and other continuous multivariate distributions; and basic multivariate statistics in the normal distribution. The second half of the text moves from defining the basics to explaining models. Topics include regression and the analysis of variance; principal components; factor analysis and latent structure analysis; canonical correlations; stable portfolio analysis; classifications and discrimination models; control in the multivariate linear model; and structuring multivariate populations, with particular focus on multidimensional scaling and clustering. In addition to its value to professional statisticians, this volume may also prove helpful to teachers and researchers in those areas of behavioral and social sciences where multivariate statistics is heavily applied. This new edition features an appendix of answers to the exercises.
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Book Title: Applied Multivariate Analysis : Using Bayesian And Frequentist Me
Number of Pages: 692 Pages
Language: English
Publication Name: Applied Multivariate Analysis : Using Bayesian and Frequentist Methods of Inference
Publisher: Dover Publications, Incorporated
Subject: Probability & Statistics / Multivariate Analysis
Publication Year: 2005
Item Height: 1.4 in
Features: Revised
Item Weight: 24.5 Oz
Type: Textbook
Item Length: 8.5 in
Subject Area: Mathematics
Author: S. James Press
Item Width: 5.4 in
Series: Dover Books on Mathematics Ser.
Format: Perfect
