Read Large Sample Covariance Matrices and High-Dimensional Data Analysis (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 39)

Large Sample Covariance Matrices and High-Dimensional Data Analysis (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 39)

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Description:High-dimensional data appear in many fields, and their analysis has become increasingly important in modern statistics. However, it has long been observed that several well-known methods in multivariate analysis become inefficient, or even misleading, when the data dimension p is larger than, say, several tens. A seminal example is the well-known inefficiency of Hotelling's T2-test in such cases. This example shows that classical large sample limits may no longer hold for high-dimensional data; statisticians must seek new limiting theorems in these instances. Thus, the theory of random matrices (RMT) serves as a much-needed and welcome alternative framework. Based on the authors' own research, this book provides a first-hand introduction to new high-dimensional statistical methods derived from RMT. The book begins with a detailed introduction to useful tools from RMT, and then presents a series of high-dimensional problems with solutions provided by RMT methods.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Large Sample Covariance Matrices and High-Dimensional Data Analysis (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 39). To get started finding Large Sample Covariance Matrices and High-Dimensional Data Analysis (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 39), you are right to find our website which has a comprehensive collection of manuals listed.
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Format: PDF, EPUB & Kindle Edition

Publisher: —

Release: 2015

ISBN: 1107065178

Description: High-dimensional data appear in many fields, and their analysis has become increasingly important in modern statistics. However, it has long been observed that several well-known methods in multivariate analysis become inefficient, or even misleading, when the data dimension p is larger than, say, several tens. A seminal example is the well-known inefficiency of Hotelling's T2-test in such cases. This example shows that classical large sample limits may no longer hold for high-dimensional data; statisticians must seek new limiting theorems in these instances. Thus, the theory of random matrices (RMT) serves as a much-needed and welcome alternative framework. Based on the authors' own research, this book provides a first-hand introduction to new high-dimensional statistical methods derived from RMT. The book begins with a detailed introduction to useful tools from RMT, and then presents a series of high-dimensional problems with solutions provided by RMT methods.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Large Sample Covariance Matrices and High-Dimensional Data Analysis (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 39). To get started finding Large Sample Covariance Matrices and High-Dimensional Data Analysis (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 39), you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Large Sample Covariance Matrices and High-Dimensional Data Analysis (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 39)

Large Sample Covariance Matrices and High-Dimensional Data Analysis (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 39)

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