Discussion of "Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation"

被引：2

作者：

Paul, Debashis ^{[1
]}

Wang, Lili ^{[2
]}

机构：

[1] Univ Calif Davis, Dept Stat, Davis, CA 95616 USA

[2] Univ Melbourne, Dept Math & Stat, Parkville, Vic 3010, Australia

来源：

ELECTRONIC JOURNAL OF STATISTICS | 2016年 / 10卷 / 01期

基金：

美国国家科学基金会;

关键词：

Covariance matrix; principal component analysis; spiked covariance model; Stieltjes transform; MODELS; ASYMPTOTICS;

D O I：

10.1214/15-EJS1019

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 discussion, we present a brief overview of recent works on the behavior of summary statistics for high-dimensional observations that are time-dependent, and the inference on parameters associated with high-dimensional time series, with emphasis on covariance and auto-covariance matrices.

引用

页码：74 / 80

页数：7

共 17 条

[1]

[Anonymous], 2010, SPRINGER SER STAT

[2]

[Anonymous], 2014, THESIS

[3] On sample eigenvalues in a generalized spiked population model [J].