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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.

SUBMITTER: Vexler A

PROVIDER: S-EPMC4191747 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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A Simple Density-Based Empirical Likelihood Ratio Test for Independence.

Vexler Albert A Tsai Wan-Min WM Hutson Alan D AD

The American statistician 20140101 3

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 gen ...[more]

PMID: 25308974

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Project description:BackgroundThe study of ancestral alleles provides insights into the evolutionary history, selection, and genetic structures of a population. In cattle, ancestral alleles are widely used in genetic analyses, including the detection of signatures of selection, determination of breed ancestry, and identification of admixture. Having a comprehensive list of ancestral alleles is expected to improve the accuracy of these genetic analyses. However, the list of ancestral alleles in cattle, especially at the whole genome sequence level, is far from complete. In fact, the current largest list of ancestral alleles (~ 42 million) represents less than 28% of the total number of detected variants in cattle. To address this issue and develop a genomic resource for evolutionary studies, we determined ancestral alleles in cattle by comparing prior derived whole-genome sequence variants to an out-species group using a population-based likelihood ratio test.ResultsOur study determined and makes available the largest list of ancestral alleles in cattle to date (70.1 million) and includes 2.3 million on the X chromosome. There was high concordance (97.6%) of the determined ancestral alleles with those from previous studies when only high-probability ancestral alleles were considered (29.8 million positions) and another 23.5 million high-confidence ancestral alleles were novel, expanding the available reference list to improve the accuracies of genetic analyses involving ancestral alleles. The high concordance of the results with previous studies implies that our approach using genomic sequence variants and a likelihood ratio test to determine ancestral alleles is appropriate.ConclusionsConsidering the high concordance of ancestral alleles across studies, the ancestral alleles determined in this study including those not previously listed, particularly those with high-probability estimates, may be used for further genetic analyses with reasonable accuracy. Our approach that used predetermined variants in species and the likelihood ratio test to determine ancestral alleles is applicable to other species for which sequence level genotypes are available.

Dataset Information

A Simple Density-Based Empirical Likelihood Ratio Test for Independence.

Publications

A Simple Density-Based Empirical Likelihood Ratio Test for Independence.

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