Information Theoretic Models for Dependence Analysis and Missing Data Estimation

Abstract

In the present chapter information theoretic dependence measure has been defined using maximum entropy principle, which measures amount of dependence among the attributes in a contingency table. A relation between information theoretic measure of dependence and Chi-square statistic has been discussed. A generalization of this information theoretic dependence measure has been also studied. In the end Yate’s method and maximum entropy estimation of missing data in design of experiment have been described and illustrated by considering practical problems with empirical data. An algorithm to estimate the missing values in a fuzzy matrix is defined and applied to estimate of missing data in contingency table.