A First Course in Multivariate Statistics (Springer Texts in Statistics)

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.

 

From a review:

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

"... is actually a very unique book that differs considerably from other multivariate texts. Flury should be applauded for his intention and effort to produce a new type of multivariate book that is neither a comprehensive theoretical treatise nor an encyclopedic methods cookbook. ... it is a welcome addition to the multivariate statistics literature. This is a well-written book with vivid and lively discussions."