Statistical Estimation of Mutual Information for Mixed Model

Alexander Bulinski () and Alexey Kozhevin ()
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Alexander Bulinski: Steklov Mathematical Institute of Russian Academy of Sciences
Alexey Kozhevin: Lomonosov Moscow State University

Methodology and Computing in Applied Probability, 2021, vol. 23, issue 1, 123-142

Abstract: Abstract Asymptotic unbiasedness and L2-consistency are established for various statistical estimates of mutual information in the mixed models framework. Such models are important, e.g., for analysis of medical and biological data. The study of the conditional Shannon entropy as well as new results devoted to statistical estimation of the differential Shannon entropy are employed essentially. Theoretical results are completed by computer simulations for logistic regression model with different parameters. The numerical experiments demonstrate that new statistics, proposed by the authors, have certain advantages.

Keywords: Mixed model; Mutual information; Statistical estimates asymptotic behavior; 60F25; 62G05; 62G20 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11009-020-09802-0

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