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TESTING FOR INDEPENDENCE OF LARGE DIMENSIONAL VECTORS
Stockholm University, Faculty of Science, Department of Mathematics.
Number of Authors: 32019 (English)In: Annals of Statistics, ISSN 0090-5364, E-ISSN 2168-8966, Vol. 47, no 5, p. 2977-3008Article in journal (Refereed) Published
Abstract [en]

In this paper, new tests for the independence of two high-dimensional vectors are investigated. We consider the case where the dimension of the vectors increases with the sample size and propose multivariate analysis of variance-type statistics for the hypothesis of a block diagonal covariance matrix. The asymptotic properties of the new test statistics are investigated under the null hypothesis and the alternative hypothesis using random matrix theory. For this purpose, we study the weak convergence of linear spectral statistics of central and (conditionally) noncentral Fisher matrices. In particular, a central limit theorem for linear spectral statistics of large dimensional (conditionally) noncentral Fisher matrices is derived which is then used to analyse the power of the tests under the alternative. The theoretical results are illustrated by means of a simulation study where we also compare the new tests with several alternative, in particular with the commonly used corrected likelihood ratio test. It is demonstrated that the latter test does not keep its nominal level, if the dimension of one sub-vector is relatively small compared to the dimension of the other sub-vector. On the other hand, the tests proposed in this paper provide a reasonable approximation of the nominal level in such situations. Moreover, we observe that one of the proposed tests is most powerful under a variety of correlation scenarios.

Place, publisher, year, edition, pages
2019. Vol. 47, no 5, p. 2977-3008
Keywords [en]
Testing for independence, large dimensional covariance matrix, noncentral Fisher random matrix, linear spectral statistics, asymptotic normality
National Category
Mathematics
Identifiers
URN: urn:nbn:se:su:diva-171931DOI: 10.1214/18-AOS1771ISI: 000478686900019OAI: oai:DiVA.org:su-171931DiVA, id: diva2:1349775
Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2019-09-10Bibliographically approved

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