title: A Simple Density-Based Empirical Likelihood Ratio Test for Independence.
abstract: We develop a novel nonparametric likelihood ratio test for independence between two random variables using a technique that is free of the common constraints of defining a given set of specific dependence structures. Our methodology revolves around an exact density-based empirical likelihood ratio test statistic that approximates in a distribution-free fashion the corresponding most powerful parametric likelihood ratio test. We demonstrate that the proposed test is very powerful in detecting general structures of dependence between two random variables, including non-linear and/or random-effect dependence structures. An extensive Monte Carlo study confirms that the proposed test is superior to the classical nonparametric procedures across a variety of settings. The real-world applicability of the proposed test is illustrated using data from a study of biomarkers associated with myocardial infarction.
authors.affiliation: Department of Biostatistics, The State University of New York at Buffalo, Buffalo, NY 14214, U.S.A.
authors.forename: Albert
authors.initials: A
authors.lastname: Vexler
authors.affiliation: Department of Biostatistics, The State University of New York at Buffalo, Buffalo, NY 14214, U.S.A.
authors.forename: Wan-Min
authors.initials: WM
authors.lastname: Tsai
authors.affiliation: Department of Biostatistics, The State University of New York at Buffalo, Buffalo, NY 14214, U.S.A.
authors.forename: Alan D
authors.initials: AD
authors.lastname: Hutson
keywords: Density-based empirical likelihood; Empirical likelihood; Independence test; Likelihood; Nonlinear dependence; Nonparametric test; Random effect