Random matrix theory in statistics: A review

被引：112
作者：
Paul, Debashis [1 ]
Aue, Alexander [1 ]
机构：
[1] Univ Calif Davis, Dept Stat, Davis, CA 95616 USA
来源：
JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2014年 / 150卷
基金：
美国国家科学基金会;
关键词：
MANOVA; Marcenko-Pastur law; Principal component analysis; Stieltjes transform; Wishart matrix; Tracy-Widom law; LIMITING SPECTRAL DISTRIBUTION; SAMPLE COVARIANCE MATRICES; PRINCIPAL COMPONENT ANALYSIS; LOCAL EIGENVALUE STATISTICS; FINITE RANK DEFORMATIONS; DYNAMIC FACTOR MODEL; TRACY-WIDOM LIMITS; EMPIRICAL DISTRIBUTION; CONVERGENCE RATE; ENERGY-LEVELS;
D O I：
10.1016/j.jspi.2013.09.005
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
We give an overview of random matrix theory (RMT) with the objective of highlighting the results and concepts that have a growing impact in the formulation and inference of statistical models and methodologies. This paper focuses on a number of application areas especially within the field of high-dimensional statistics and describes how the development of the theory and practice in high-dimensional statistical inference has been influenced by the corresponding developments in the field of RMT. (C) 2014 Elsevier B.V. All rights reserved.
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页码：1 / 29
页数：29
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