Statistical Estimation of Mutual Information for Mixed Model

被引：0
|
作者：
Alexander Bulinski
Alexey Kozhevin
机构：
[1] Steklov Mathematical Institute of Russian Academy of Sciences,Department of Mathematics and Mechanics
[2] Lomonosov Moscow State University,undefined
来源：
Methodology and Computing in Applied Probability | 2021年 / 23卷
关键词：
Mixed model; Mutual information; Statistical estimates asymptotic behavior; 60F25; 62G05; 62G20;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：123 / 142
页数：19
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