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Sample Covariance Matrices and the Marčenko-Pastur Law

  • Zhidong Bai
  • Jack W. Silverstein
Chapter
Part of the Springer Series in Statistics book series (SSS)

Abstract

The sample covariance matrix is a most important random matrix in multivariate statistical inference. It is fundamental in hypothesis testing, principal component analysis, factor analysis, and discrimination analysis. Many test statistics are defined by its eigenvalues.

Keywords

Characteristic Sequence Sample Covariance Single Edge Sample Covariance Matrix Sample Covariance Matrice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.School of Mathematics and Statistics KLAS MOE Northeast Normal UniversityChangchunChina
  2. 2.Department of Statistics and Applied ProbabilityNational University of SingaporeSingaporeSingapore
  3. 3.Department of MathematicsNorth Carolina State UniversityRaleighUS

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