CLT for linear spectral statistics of high-dimensional sample covariance matrices in elliptical distributions

被引：2
|
作者：
Zhang, Yangchun [1 ]
Hu, Jiang [2 ]
Li, Weiming [3 ]
机构：
[1] Shanghai Univ, Dept Math, Shanghai 200444, Peoples R China
[2] Northeast Normal Univ, KLASMOE & Sch Math & Stat, Changchun 130024, Peoples R China
[3] Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China
来源：
JOURNAL OF MULTIVARIATE ANALYSIS | 2022年 / 191卷
基金：
国家重点研发计划; 中国国家自然科学基金;
关键词：
Confidence interval; Covariance matrix; Elliptical distribution; Gaussian scale mixture; High-dimensional data; CENTRAL-LIMIT-THEOREM; EMPIRICAL DISTRIBUTION; EIGENVALUES;
D O I：
10.1016/j.jmva.2022.105007
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：
In this paper, we establish a new central limit theorem for the linear spectral statistics of high-dimensional sample covariance matrices. The underlying population belongs to the family of elliptical distributions, and the dimension of the population is allowed to grow to infinity, in proportion to the sample size. As an application, we construct confidence intervals for the model parameters of a Gaussian scale mixture.(C) 2022 Elsevier Inc. All rights reserved.
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页数：20
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