A mutual information estimator with exponentially decaying bias

Zhang Zhiyi () and Zheng Lukun
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Zhang Zhiyi: Department of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Zheng Lukun: Department of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA

Statistical Applications in Genetics and Molecular Biology, 2015, vol. 14, issue 3, 243-252

Abstract: A nonparametric estimator of mutual information is proposed and is shown to have asymptotic normality and efficiency, and a bias decaying exponentially in sample size. The asymptotic normality and the rapidly decaying bias together offer a viable inferential tool for assessing mutual information between two random elements on finite alphabets where the maximum likelihood estimator of mutual information greatly inflates the probability of type I error. The proposed estimator is illustrated by three examples in which the association between a pair of genes is assessed based on their expression levels. Several results of simulation study are also provided.

Keywords: asymptotic normality; bias; mle; mutual information; nonparametric estimator (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1515/sagmb-2014-0047

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