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On mutual information estimation for mixed-pair random variables

Author

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  • Beknazaryan, Aleksandr
  • Dang, Xin
  • Sang, Hailin

Abstract

We study the mutual information estimation for mixed-pair random variables. One random variable is discrete and the other one is continuous. We develop a kernel method to estimate the mutual information between the two random variables. The estimates enjoy a central limit theorem under some regular conditions on the distributions. The theoretical results are demonstrated by simulation study.

Suggested Citation

  • Beknazaryan, Aleksandr & Dang, Xin & Sang, Hailin, 2019. "On mutual information estimation for mixed-pair random variables," Statistics & Probability Letters, Elsevier, vol. 148(C), pages 9-16.
    • Handle: RePEc:eee:stapro:v:148:y:2019:i:c:p:9-16
    DOI: 10.1016/j.spl.2018.12.011
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    References listed on IDEAS

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    1. Peter Hall & Sally Morton, 1993. "On the estimation of entropy," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 45(1), pages 69-88, March.
    2. Harry Joe, 1989. "Estimation of entropy and other functionals of a multivariate density," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 41(4), pages 683-697, December.
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    Cited by:

    1. David Atienza & Pedro Larrañaga & Concha Bielza, 2022. "Rejoinder on: Hybrid semiparametric Bayesian networks," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 344-347, June.

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