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A significance test of the RV coefficient in high dimensions
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2019 (English)In: Computational Statistics & Data Analysis, ISSN 0167-9473, E-ISSN 1872-7352, Vol. 131, p. 116-130Article in journal (Refereed) Published
Abstract [en]

The RV coefficient is an important measure of linear dependence between two multivariate data vectors. Using unbiased and computationally efficient estimators of its components, a modification to the RV coefficient is proposed, and used to construct a test of significance for the true coefficient. The modified estimator improves the accuracy of the original and, along with the test, can be applied to data with arbitrarily large dimensions, possibly exceeding the sample size, and the underlying distribution need only have finite fourth moment. Exact and asymptotic properties are studied under fairly general conditions. The accuracy of the modified estimator and the test is shown through simulations under a variety of parameter settings. In comparisons against several existing methods, both the proposed estimator and the test exhibit similar performance to the distance correlation. Several real data applications are also provided.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2019. Vol. 131, p. 116-130
Keywords [en]
RV coefficient, Dependency measure, Cross-correlations, High-dimensional inference
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-371105DOI: 10.1016/j.csda.2018.10.008ISI: 000450384700011OAI: oai:DiVA.org:uu-371105DiVA, id: diva2:1272835
Available from: 2018-12-20 Created: 2018-12-20 Last updated: 2018-12-20Bibliographically approved

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Ahmad, M. Rauf

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