EconPapers    
Economics at your fingertips  
 

Robust two-sample test of high-dimensional mean vectors under dependence

Wei Wang, Nan Lin and Xiang Tang

Journal of Multivariate Analysis, 2019, vol. 169, issue C, 312-329

Abstract: A basic problem in modern multivariate analysis is testing the equality of two mean vectors in settings where the dimension p increases with the sample size n. This paper proposes a robust two-sample test for high-dimensional data against sparse and strong alternatives, in which the mean vectors of the populations differ in only a few dimensions, but the magnitude of the differences is large. The test is based on trimmed means and robust precision matrix estimators. The asymptotic joint distribution of the trimmed means is established, and the proposed test statistic is shown to have a Gumbel distribution in the limit. Simulation studies suggest that the numerical performance of the proposed test is comparable to that of non-robust tests for uncontaminated data. For cell-wise contaminated data, it outperforms non-robust tests. An illustration involves biomarker identification in an Alzheimer’s disease dataset.

Keywords: Cell-wise contamination; Robust precision matrix estimation; Sparse and strong alternatives; Two-sample mean test; Trimmed mean (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047259X17307601
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:169:y:2019:i:c:p:312-329

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

 
Page updated 2019-01-19
Handle: RePEc:eee:jmvana:v:169:y:2019:i:c:p:312-329