Multivariate Statistics: High-Dimensional and Large-Sample Approximations

✍ Scribed by Yasunori Fujikoshi, Vladimir V. Ulyanov, Ryoichi Shimizu(auth.)

• Year: 2010
• Tongue: English
• Leaves: 553
• Series: Wiley Series in Probability and Statistics
• Category: Library

✦ Synopsis

A comprehensive examination of high-dimensional analysis of multivariate methods and their real-world applications

Multivariate Statistics: High-Dimensional and Large-Sample Approximations is the first book of its kind to explore how classical multivariate methods can be revised and used in place of conventional statistical tools. Written by prominent researchers in the field, the book focuses on high-dimensional and large-scale approximations and details the many basic multivariate methods used to achieve high levels of accuracy.

The authors begin with a fundamental presentation of the basic tools and exact distributional results of multivariate statistics, and, in addition, the derivations of most distributional results are provided. Statistical methods for high-dimensional data, such as curve data, spectra, images, and DNA microarrays, are discussed. Bootstrap approximations from a methodological point of view, theoretical accuracies in MANOVA tests, and model selection criteria are also presented. Subsequent chapters feature additional topical coverage including:

• High-dimensional approximations of various statistics
• High-dimensional statistical methods
• Approximations with computable error bound
• Selection of variables based on model selection approach
• Statistics with error bounds and their appearance in discriminant analysis, growth curve models, generalized linear models, profile analysis, and multiple comparison

Each chapter provides real-world applications and thorough analyses of the real data. In addition, approximation formulas found throughout the book are a useful tool for both practical and theoretical statisticians, and basic results on exact distributions in multivariate analysis are included in a comprehensive, yet accessible, format.

Multivariate Statistics is an excellent book for courses on probability theory in statistics at the graduate level. It is also an essential reference for both practical and theoretical statisticians who are interested in multivariate analysis and who would benefit from learning the applications of analytical probabilistic methods in statistics.Content:
Chapter 1 Multivariate Normal and Related Distributions (pages 1–28):
Chapter 2 Wishart Distribution (pages 29–46):
Chapter 3 Hotelling's T2 and Lambda Statistics (pages 47–67):
Chapter 4 Correlation Coefficients (pages 69–89):
Chapter 5 Asymptotic Expansions for Multivariate Basic Statistics (pages 91–148):
Chapter 6 MANOVA Models (pages 149–186):
Chapter 7 Multivariate Regression (pages 187–218):
Chapter 8 Classical and High?Dimensional Tests for Covariance Matrices (pages 219–247):
Chapter 9 Discriminant Analysis (pages 249–282):
Chapter 10 Principal Component Analysis (pages 283–315):
Chapter 11 Canonical Correlation Analysis (pages 317–347):
Chapter 12 Growth Curve Analysis (pages 349–378):
Chapter 13 Approximation to the Scale?Mixted Distributions (pages 379–421):
Chapter 14 Approximation to Some Related Distributions (pages 423–440):
Chapter 15 Error Bounds for Approximations of Multivariate Tests (pages 441–466):
Chapter 16 Error Bounds for Approximations to Some Other Statistics (pages 467–494):