A NEW DISCRETE ENTROPIC MODEL AND ITS APPLICATION TO CONTINGENCY TABLE INFERENCE.

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Bibliographic Details
Title:	A NEW DISCRETE ENTROPIC MODEL AND ITS APPLICATION TO CONTINGENCY TABLE INFERENCE.
Authors:	Kumari, Poonam¹ poonamkumari1865@gmail.com, Kumari, Nishi² nishiraj1510@gmail.com
Source:	Reliability: Theory & Applications. Jun2025, Vol. 20 Issue 2, p944-956. 13p.
Subjects:	Contingency tables, Inferential statistics, Statistics, Lagrange multiplier, Entropy (Information theory)
Abstract:	A variety of parametric and non-parametric information models, both discrete and continuous, are well-established. However, there remains a continued need to develop new parametric models that offer greater flexibility in analyzing complex systems. Information entropy is closely linked to the field of statistics, providing valuable insights. This paper introduces a novel discrete information entropic model and explores its applications in statistical analysis. We have demonstrated the efficacy of this new model by applying it to the inference of contingency tables, a cornerstone of statistical analysis. Using the maximum entropy principle and Lagrange method of multiplier, we have derived an important relation between the chi-square and the entropy measure. By incorporating this novel entropy measure, we have expanded the scope and applicability of the maximum entropy principle in this domain, enabling more robust and informative statistical inferences. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

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Abstract:	A variety of parametric and non-parametric information models, both discrete and continuous, are well-established. However, there remains a continued need to develop new parametric models that offer greater flexibility in analyzing complex systems. Information entropy is closely linked to the field of statistics, providing valuable insights. This paper introduces a novel discrete information entropic model and explores its applications in statistical analysis. We have demonstrated the efficacy of this new model by applying it to the inference of contingency tables, a cornerstone of statistical analysis. Using the maximum entropy principle and Lagrange method of multiplier, we have derived an important relation between the chi-square and the entropy measure. By incorporating this novel entropy measure, we have expanded the scope and applicability of the maximum entropy principle in this domain, enabling more robust and informative statistical inferences. [ABSTRACT FROM AUTHOR]
ISSN:	19322321