What is it about?
The Information theoretic models for dependence analysis and estimation of the missing data are very important area in many fields of research like agriculture, economics, and science laboratory and data management. Various scientists and technocrats have suggested different methods and one is the max entropy method described by Kapur [3] and that have played better role than Yate [5] method for estimating the missing data in design of experiment.
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Why is it important?
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.
Perspectives
When some observed values are found missing due to unnatural climate or any other reason and is very difficult to repeat the experiment, for example the field trials of agriculture crops are disrupted due to hailstones. Then, some technique to predict or estimate these values is required to be developed. Contingency tables have applications in economics, agriculture, and laboratory and data management to study independence of attributes. Kullback [9] discussed contingency tables from MDI principle point of view. Ku and Kullback [10] used a similar approach to discuss interactions in multidimensional contingency tables.
Prof. D S Hooda
Guru Jambheshwar University of Science and Technology
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This page is a summary of: Information Theoretic Models for Dependence Analysis and Missing Data Estimation, June 2021, Sciencedomain International,
DOI: 10.9734/bpi/castr/v5/1744c.
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