The Annals of Statistics

Nonparametric estimate of spectral density functions of sample covariance matrices: A first step

Bing-Yi Jing, Guangming Pan, Qi-Man Shao, and Wang Zhou

Full-text: Open access

Abstract

The density function of the limiting spectral distribution of general sample covariance matrices is usually unknown. We propose to use kernel estimators which are proved to be consistent. A simulation study is also conducted to show the performance of the estimators.

Article information

Source
Ann. Statist., Volume 38, Number 6 (2010), 3724-3750.

Dates
First available in Project Euclid: 30 November 2010

Permanent link to this document
https://projecteuclid.org/euclid.aos/1291126971

Digital Object Identifier
doi:10.1214/10-AOS833

Mathematical Reviews number (MathSciNet)
MR2766866

Zentralblatt MATH identifier
1204.62056

Subjects
Primary: 15A52 60F15: Strong theorems 62E20: Asymptotic distribution theory
Secondary: 60F17: Functional limit theorems; invariance principles

Keywords
Sample covariance matrices Stieltjes transform nonparametric estimate

Citation

Jing, Bing-Yi; Pan, Guangming; Shao, Qi-Man; Zhou, Wang. Nonparametric estimate of spectral density functions of sample covariance matrices: A first step. Ann. Statist. 38 (2010), no. 6, 3724--3750. doi:10.1214/10-AOS833. https://projecteuclid.org/euclid.aos/1291126971


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