Bernoulli

  • Bernoulli
  • Volume 24, Number 3 (2018), 1787-1833.

On Gaussian comparison inequality and its application to spectral analysis of large random matrices

Fang Han, Sheng Xu, and Wen-Xin Zhou

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Abstract

Recently, Chernozhukov, Chetverikov, and Kato (Ann. Statist. 42 (2014) 1564–1597) developed a new Gaussian comparison inequality for approximating the suprema of empirical processes. This paper exploits this technique to devise sharp inference on spectra of large random matrices. In particular, we show that two long-standing problems in random matrix theory can be solved: (i) simple bootstrap inference on sample eigenvalues when true eigenvalues are tied; (ii) conducting two-sample Roy’s covariance test in high dimensions. To establish the asymptotic results, a generalized $\varepsilon$-net argument regarding the matrix rescaled spectral norm and several new empirical process bounds are developed and of independent interest.

Article information

Source
Bernoulli, Volume 24, Number 3 (2018), 1787-1833.

Dates
Received: November 2015
Revised: July 2016
First available in Project Euclid: 2 February 2018

Permanent link to this document
https://projecteuclid.org/euclid.bj/1517540460

Digital Object Identifier
doi:10.3150/16-BEJ912

Mathematical Reviews number (MathSciNet)
MR3757515

Zentralblatt MATH identifier
06839252

Keywords
extreme value theory Gaussian comparison inequality random matrix theory Roy’s largest root test spectral analysis

Citation

Han, Fang; Xu, Sheng; Zhou, Wen-Xin. On Gaussian comparison inequality and its application to spectral analysis of large random matrices. Bernoulli 24 (2018), no. 3, 1787--1833. doi:10.3150/16-BEJ912. https://projecteuclid.org/euclid.bj/1517540460


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