A First Course in Multivariate Statistics

Bernard Flury - Collection Springer texts in statistics

714 pages, parution le 05/08/2004

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Résumé

A comprehensive and self-contained introduction to the field, carefully balancing mathematical theory and practical applications. It starts at an elementary level, developing concepts of multivariate distributions from first principles. After a chapter on the multivariate normal distribution reviewing the classical parametric theory, methods of estimation are explored using the plug-in principles as well as maximum likelihood. Two chapters on discrimination and classification, including logistic regression, form the core of the book, followed by methods of testing hypotheses developed from heuristic principles, likelihood ratio tests and permutation tests. Finally, the powerful self-consistency principle is used to introduce principal components as a method of approximation, rounded off by a chapter on finite mixture analysis.

Written for:
Researchers Grad students

Sommaire

Why Multivariate Statistics?
Joint Distribution of Several Random Variables
The Multivariate Normal Distribution
Parameter Estimation
Discrimination and Classification, Round 1
Statistical Inference for Means
Discrimination and Classification, Round 2
Linear Principal Component Analysis
Normal Mixtures
A. Selected Results from Matrix Algebra

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Bernard Flury
Collection	Springer texts in statistics
Parution	05/08/2004
Nb. de pages	714
Format	21 x 24
Couverture	Relié
Poids	1535g
Intérieur	Noir et Blanc
EAN13	9780387982069
ISBN13	978-0-387-98206-9