On the orthogonally equivariant estimators of a covariance matrix

Fuente: arXiv
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Main Authors: Tsai, Ming-Tien, Tsai, Chia-Hsuan
Format: Preprint
Published: 2024
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author Tsai, Ming-Tien
Tsai, Chia-Hsuan
author_facet Tsai, Ming-Tien
Tsai, Chia-Hsuan
contents In this note, when the dimension $p$ is large we look into the insight of the Mar$\check{c}$enko-Pastur equation to get an explicit equality relationship, and use the obtained equality to establish a new kind of orthogonally equivariant estimator of the population covariance matrix. Under some regularity conditions, the proposed novel estimators of the population eigenvalues are shown to be consistent for the eigenvalues of population covariance matrix. It is also shown that the proposed estimator is the best orthogonally equivariant estimator of population covariance matrix under the normalized Stein loss function.
format Preprint
id arxiv_https___arxiv_org_abs_2405_06877
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle On the orthogonally equivariant estimators of a covariance matrix
Tsai, Ming-Tien
Tsai, Chia-Hsuan
Statistics Theory
In this note, when the dimension $p$ is large we look into the insight of the Mar$\check{c}$enko-Pastur equation to get an explicit equality relationship, and use the obtained equality to establish a new kind of orthogonally equivariant estimator of the population covariance matrix. Under some regularity conditions, the proposed novel estimators of the population eigenvalues are shown to be consistent for the eigenvalues of population covariance matrix. It is also shown that the proposed estimator is the best orthogonally equivariant estimator of population covariance matrix under the normalized Stein loss function.
title On the orthogonally equivariant estimators of a covariance matrix
topic Statistics Theory
url https://arxiv.org/abs/2405.06877