Applied Multivariate Analysis : Using Bayesian And Frequentist Methods Of Inference

Press, S. James

ISBN 10: 0486442365 / ISBN 13: 9780486442365

Editore: Dover, 2005

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Titolo: Applied Multivariate Analysis : Using ...

Casa editrice: Dover

Data di pubblicazione: 2005

Legatura: Brossura

Condizione: As New

Edizione: seconda edizione

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Riassunto:

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.

Contenuti:

Preface Notation I. Introduction 1. Foundations 2. Matrix Theory Useful in Multivariate Analysis 3. Continuous Multivariate Distributions, The Normal Distribution, Bayesian Inference 4. Multivariate Large Sample Distributions and Approximations 5. The Wishart and Related Distributions 6. Other Continuous Multivariate Distributions 7. Basic Multivariate Statistics in the Normal Distribution II. Models 8. Regression and the Analysis of Variance 9. Principal Components 10. Factor Analysis and Latent Structure Analysis 11. Canonical Correlations 12. Stable Portfolio Analysis 13. Classifications and Discrimination Models 14. Control in the Multivariate Linear Model 15. Structuring Multivariate Populations (Multidimensional Scaling and Clustering) Appendixes Index