Bayesian nonparametric test for independence between random vectors

Zichen Ma and Timothy E. Hanson

Computational Statistics & Data Analysis, 2020, vol. 149, issue C

Abstract: A nonparametric approach for testing independence among groups of continuous random variables is proposed. Gaussian-centered multivariate finite Polya tree priors are used to model the underlying probability distributions. Integrating out the random probability measure, a tractable empirical Bayes factor is derived and used as the test statistic. The Bayes factor is consistent in the sense that it tends to infinity under the alternative, and zero under the null. A p-value is then obtained through a permutation test based on the observed Bayes factor. Through a series of simulation studies, the performance of the proposed approach is examined and compared to several existing approaches based on the power of the test as well as the observed Bayes factor. Lastly, the proposed method is applied to a set of real data in ecology.

Keywords: Independence test; Polya tree; Density estimation; Bayes factor; Permutation test (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:149:y:2020:i:c:s0167947320300505

DOI: 10.1016/j.csda.2020.106959

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