Multivariate Statistical Analysis - A High-Dimensional Approach (Hardcover, 2000 ed.)

This book presents a new branch of mathematical statistics aimed at constructing unimprovable methods of multivariate analysis, multi-parametric estimation, and discriminant and regression analysis. In contrast to the traditional consistent Fisher method of statistics, the essentially multivariate technique is based on the decision function approach by A. Wald. Developing this new method for high dimensions, comparable in magnitude with sample size, provides stable approximately unimprovable procedures in some wide classes, depending on an arbitrary function. A remarkable fact is established: for high-dimensional problems, under some weak restrictions on the variable dependence, the standard quality functions of regularized multivariate procedures prove to be independent of distributions. For the first time in the history of statistics, this opens the possibility to construct unimprovable procedures free from distributions. Audience: This work will be of interest to researchers and graduate students whose work involves statistics and probability, reliability and risk analysis, econometrics, machine learning, medical statistics, and various applications of multivariate analysis.

General

Imprint:	Springer
Country of origin:	Netherlands
Series:	Theory and Decision Library B, 41
Release date:	2001
First published:	2000
Authors:	V.I. Serdobolskii
Dimensions:	234 x 156 x 25mm (L x W x T)
Format:	Hardcover
Pages:	244
Edition:	2000 ed.
ISBN-13:	978-0-7923-6643-0
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics
LSN:	0-7923-6643-3
Barcode:	9780792366430

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