A mutual information estimator with exponentially decaying bias

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 bet...

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Bibliographic Details
Published inStatistical applications in genetics and molecular biology Vol. 14; no. 3; pp. 243 - 252
Main Authors Zhang, Zhiyi, Zheng, Lukun
Format Journal Article
LanguageEnglish
Published Germany De Gruyter 01.06.2015
Subjects
62F12
62G05
62G20
Algorithms
asymptotic normality
bias
mle
Models, Theoretical
mutual information
nonparametric estimator
Primary 62F10
Online AccessGet full text
ISSN2194-6302
1544-6115
DOI10.1515/sagmb-2014-0047

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Summary: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.
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ISSN:2194-6302
1544-6115
DOI:10.1515/sagmb-2014-0047