Robust two-sample test of high-dimensional mean vectors under dependence
type: Item
instance of: scholarly article
title: Robust two-sample test of high-dimensional mean vectors under dependence
zbMATH Open document ID: 1415.62041
author: Xianqiang Yang
published in: Journal of Multivariate Analysis
publication date: 2019-01-04T00:00:00Z
review text: Consider testing the equality of the means \(\mu_1,\mu_2\) of two normal distributed random vectors \(X,Y\) having the same covariance matrix \(\Sigma\). Here, the problem is treated that the dimension \(p\), of the random vectors is comparable to, or even larger than the size \(n\) of the samples of \(X,Y\). Since, in this case, standard tests, as Hotelling's \(T^2\) test, have a low performance and are sensitive to outliers, a more robust test statistic is suggested based on the Kolmogorov distance, adopting trimmed means and robust precision matrix estimators. A simulation study is given.
reviewed by: Kurt Marti
Mathematics Subject Classification ID: 62H15
Mathematics Subject Classification ID: 62G35
zbMATH DE Number: 6997760
zbMATH Keywords: two-sample mean tests
zbMATH Keywords: high dimension of mean vectors
zbMATH Keywords: robust test
describes a project that uses: AppliedPredictiveModeling
describes a project that uses: ks
MaRDI profile type: Publication
full work available at URL: j.jmva.2018.09.013
OpenAlex ID: W2895799356
cites work: Q4882268
cites work: Q4671207
cites work: On Some Robust Estimates of Location
cites work: Q5560061
cites work: A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
cites work: Two-Sample Test of High Dimensional Means Under Dependence
cites work: A two-sample test for high-dimensional data with applications to gene-set testing
cites work: Central limit theorems and bootstrap in high dimensions
cites work: Q5434185
cites work: On the derivatives of the trimmed mean
cites work: The Generalization of Student's Ratio
cites work: A review of 20 years of naive tests of significance for high-dimensional mean vectors and covariance matrices
cites work: Applied Predictive Modeling
cites work: High-dimensional robust precision matrix estimation: cellwise corruption under \(\epsilon \)-contamination
cites work: High-dimensional covariance matrix estimation with missing observations
cites work: A test for the mean vector with fewer observations than the dimension
cites work: A high-dimensional two-sample test for the mean using random subspaces
cites work: High-Dimensional Probability
cites work: A High-Dimensional Nonparametric Multivariate Test for Mean Vector
cites work: Some results on a Winsorized correlation coefficient
cites work: A high-dimension two-sample test for the mean using cluster subspaces
Wikidata QID: Q129113692
DOI: 10.1016/J.JMVA.2018.09.013
description: scientific article; zbMATH DE number 6997760