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Statistical Applications in Genetics and Molecular Biology

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Volume 14, Issue 3 (Jun 2015)

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Volume 1 (2002)

A mutual information estimator with exponentially decaying bias

Zhiyi Zhang
  • Corresponding author
  • Department of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
  • Email:
/ Lukun Zheng
  • Department of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Published Online: 2015-05-05 | DOI: https://doi.org/10.1515/sagmb-2014-0047

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

AMS 2000 Subject Classifications: Primary 62F10; 62F12; 62G05; 62G20

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About the article

Corresponding author: Zhiyi Zhang, Department of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA, e-mail:


Published Online: 2015-05-05

Published in Print: 2015-06-01


Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.1515/sagmb-2014-0047.

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