Large Sample Covariance Matrices and High-Dimensional Data Analysis

Yao, Jianfeng, Zheng, Shurong, Bai, Zhidong

  • 出版商: Cambridge
  • 出版日期: 2015-04-30
  • 售價: $3,420
  • 貴賓價: 9.5$3,249
  • 語言: 英文
  • 頁數: 322
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1107065178
  • ISBN-13: 9781107065178
  • 相關分類: Data Science
  • 已絕版

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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.