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Multivariate Statistical Inference by Narayan C. Giri; Z. W. Birnbaum; E. Lukacs is a nonfiction available to read on EtoBox.

What is Multivariate Statistical Inference about?

Multivariate Statistical Inference is a 10-chapter text that covers the theoretical and applied aspects of multivariate analysis, specifically the multivariate normal distribution using the invariance approach. Chapter I contains some special results regarding characteristic roots and vectors, and partitioned submatrices of real and complex matrices, as well as some special theorems on real and complex matrices useful in multivariate analysis. Chapter II deals with the theory of groups and related results that are useful for the development of invariant statistical test procedures, including the Jacobians of some specific transformations that are useful for deriving multivariate sampling distributions. Chapter III is devoted to basic notions of multivariate distributions and the principle of invariance in statistical testing of hypotheses. Chapters IV and V deal with the study of the real multivariate normal distribution through the probability density function and through a simple characterization and the maximum likelihood estimators of the parameters of the multivariate normal distribution and their optimum properties. Chapter VI tackles a systematic derivation of basic multivar

Who reads Multivariate Statistical Inference?

Author

Narayan C. Giri; Z. W. Birnbaum; E. Lukacs

Publisher

Academic Press, Incorporated

Published

1977

Language

EN

ISBN

9780122856501

Category

nonfiction

Subjects

Mathematics, Stem