Information Theoretic Models for Dependence Analysis And missing Data Estimation
American Journal of Theoretical and Applied Statistics
Volume 2, Issue 2, March 2013, Pages: 15-20
Received: Feb. 3, 2013; Published: Mar. 10, 2013
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Authors
D. S. Hooda, Department of Mathematics, Jaypee University of Engineering andTechnology, A.B. Road, Raghogarh, Distt.Guna-473226 (M.P.) India; Department of Statistics, University of Jammu, Jammu-(India)
Permil Kumar, Department of Mathematics, Jaypee University of Engineering andTechnology, A.B. Road, Raghogarh, Distt.Guna-473226 (M.P.) India; Department of Statistics, University of Jammu, Jammu-(India)
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Abstract
In the present communication 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.
Keywords
Maximum Entropy Principle, Contingency Table, Chi-Square Statistics, Lagrange’s Multipliers And Depen-dence Measure
To cite this article
D. S. Hooda, Permil Kumar, Information Theoretic Models for Dependence Analysis And missing Data Estimation, American Journal of Theoretical and Applied Statistics. Vol. 2, No. 2, 2013, pp. 15-20. doi: 10.11648/j.ajtas.20130202.12
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