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Location-invariant Multi-sample U-tests for Covariance Matrices with Large Dimension
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2017 (English)In: Scandinavian Journal of Statistics, ISSN 0303-6898, E-ISSN 1467-9469, Vol. 44, no 2, 500-523 p.Article in journal (Refereed) Published
Abstract [en]

For two or more multivariate distributions with common covariance matrix, test statistics for certain special structures of the common covariance matrix are presented when the dimension of the multivariate vectors may exceed the number of such vectors. The test statistics are constructed as functions of location-invariant estimators defined as U-statistics, and the corresponding asymptotic theory is used to derive the limiting distributions of the proposed tests. The properties of the test statistics are established under mild and practical assumptions, and the same are numerically demonstrated using simulation results with small or moderate sample sizes and large dimensions.

Place, publisher, year, edition, pages
2017. Vol. 44, no 2, 500-523 p.
Keyword [en]
high-dimensional inference, multi-sample sphericity, U-statistics
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-325684DOI: 10.1111/sjos.12262ISI: 000400985000010OAI: oai:DiVA.org:uu-325684DiVA: diva2:1120658
Available from: 2017-07-06 Created: 2017-07-06 Last updated: 2017-07-06Bibliographically approved

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Ahmad, M. Rauf
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CiteExportLink to record
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