Power computation for hypothesis testing with high-dimensional covariance matrices
type: Item
instance of: scholarly article
title: Power computation for hypothesis testing with high-dimensional covariance matrices
zbMATH Open document ID: 1466.62138
author: Ruitao Lin
author: Zhongying Liu
author: Shurong Zheng
author: Guosheng Yin
published in: Computational Statistics and Data Analysis
publication date: 2018-08-15T00:00:00Z
Mathematics Subject Classification ID: 62-08
Mathematics Subject Classification ID: 62H15
zbMATH DE Number: 6918024
zbMATH Keywords: central limit theorem
zbMATH Keywords: confidence interval
zbMATH Keywords: high-dimensional covariance matrix
zbMATH Keywords: hypothesis testing
zbMATH Keywords: power calculation
zbMATH Keywords: Stieltjes transform
MaRDI profile type: Publication
full work available at URL: j.csda.2016.05.008
OpenAlex ID: W2410837842
cites work: Q4420195
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cites work: Substitution principle for CLT of linear spectral statistics of high-dimensional sample covariance matrices with applications to hypothesis testing
DOI: 10.1016/J.CSDA.2016.05.008
description: scientific article; zbMATH DE number 6918024