Applications of Information Theory in Statistics
This special issue belongs to the section "Information Theory, Probability and Statistics".
Special Issue Information
Dear Colleagues,
It is well known that several important topics in statistics are highly related to information theory—maximum likelihood and cross entropy, Akaike information criterion and Kullback–Leibler information, sum of squares and mutual information, etc. Though Fisher information is the most important measure of information in parametric statistical inference and related science, information theory is a popular topic related to nonparametric statistical inference and machine learning methods in statistics today. Thus, gathering relevant works on the applications of information theory in statistics is of growing importance.
This Special Issue aims to serve as a forum for the interpretation of information theory in terms of statistics. All topics related to information theory and statistics, which include the applications of information theory in statistics or applications of statistical concepts to information theory, fall within the scope of this Special Issue.
Prof. Sangun Park
Guest Editor
Manuscript Submission Information
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Keywords
- estimation and testing
- information theory
- statistics
- applications
- Fisher information
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Published Papers
Entropy - ISSN 1099-4300