A necessary test for complete independence in high dimensions using rank-correlations

Guanghui Wang, Changliang Zou and Zhaojun Wang

Journal of Multivariate Analysis, 2013, vol. 121, issue C, 224-232

Abstract: We propose a nonparametric necessary test for the complete independence of random variables in high-dimensional environment. The test is constructed based on Spearman’s rank-correlations and is shown to be asymptotically normal by the martingale central limit theorem as both the sample size and the dimension of variables go to infinity. Simulation studies show that the proposed test works well in finite-sample situations.

Keywords: Asymptotic normality; Complete independence; High-dimensional problem; Necessary tests; Spearman’s rank-correlation (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.jmva.2013.05.014

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