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Statistical Estimation of Mutual Information for Mixed Model

Author

Listed:
  • Alexander Bulinski

    (Steklov Mathematical Institute of Russian Academy of Sciences)

  • Alexey Kozhevin

    (Lomonosov Moscow State University)

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.

Suggested Citation

  • Alexander Bulinski & Alexey Kozhevin, 2021. "Statistical Estimation of Mutual Information for Mixed Model," Methodology and Computing in Applied Probability, Springer, vol. 23(1), pages 123-142, March.
    • Handle: RePEc:spr:metcap:v:23:y:2021:i:1:d:10.1007_s11009-020-09802-0
    DOI: 10.1007/s11009-020-09802-0
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