2015/01/28 by Yimin Kao, Kao, Yimin, Brian J. Reich +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Gene expression and cancer classification #Methodology (stat.ME) #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.1501.07198
openalex publication_date 2015/01/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this article, we propose a new method for the fundamental task of testing for dependence between two groups of variables. The response densities under the null hypothesis of independence and the alternative hypothesis of dependence are specified by nonparametric Bayesian models. Under the null hypothesis, the joint distribution is modeled by the product of two independent Dirichlet Process Mixture (DPM) priors; under the alternative, the full joint density is modeled by a multivariate DPM prior. The test is then based on the posterior probability of favoring the alternative hypothesis. The proposed test not only has good performance for testing linear dependence among other popular nonparametric tests, but is also preferred to other methods in testing many of the nonlinear dependencies we explored. In the analysis of gene expression data, we compare different methods for testing pairwise dependence between genes. The results show that the proposed test identifies some dependence structures that are not detected by other tests.