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

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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]
Copyright of Reliability: Theory & Applications is the property of International Group on Reliability and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A NEW DISCRETE ENTROPIC MODEL AND ITS APPLICATION TO CONTINGENCY TABLE INFERENCE.
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  Data: <searchLink fieldCode="AR" term="%22Kumari%2C+Poonam%22">Kumari, Poonam</searchLink><relatesTo>1</relatesTo><i> poonamkumari1865@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kumari%2C+Nishi%22">Kumari, Nishi</searchLink><relatesTo>2</relatesTo><i> nishiraj1510@gmail.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Reliability%3A+Theory+%26+Applications%22">Reliability: Theory & Applications</searchLink>. Jun2025, Vol. 20 Issue 2, p944-956. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Contingency+tables%22">Contingency tables</searchLink><br /><searchLink fieldCode="DE" term="%22Inferential+statistics%22">Inferential statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Lagrange+multiplier%22">Lagrange multiplier</searchLink><br /><searchLink fieldCode="DE" term="%22Entropy+%28Information+theory%29%22">Entropy (Information theory)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Reliability: Theory & Applications is the property of International Group on Reliability and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Inferential statistics
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      – SubjectFull: Statistics
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      – SubjectFull: Lagrange multiplier
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      – SubjectFull: Entropy (Information theory)
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      – TitleFull: A NEW DISCRETE ENTROPIC MODEL AND ITS APPLICATION TO CONTINGENCY TABLE INFERENCE.
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              Text: Jun2025
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