On residual cumulative generalized exponential entropy and its application in human health

Numerous adaptations of traditional entropy concepts and their residual counterparts have emerged in statistical research. While some methodologies incorporate supplementary variables or reshape foundational assumptions, many ultimately align with conventional formulations. This study introduces a n...

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Main Authors: Hanan H. Sakr, Mohamed S. Mohamed
Format: Article
Language:English
Published: AIMS Press 2025-03-01
Series:Electronic Research Archive
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Online Access:https://www.aimspress.com/article/doi/10.3934/era.2025077
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author Hanan H. Sakr
Mohamed S. Mohamed
author_facet Hanan H. Sakr
Mohamed S. Mohamed
author_sort Hanan H. Sakr
collection DOAJ
description Numerous adaptations of traditional entropy concepts and their residual counterparts have emerged in statistical research. While some methodologies incorporate supplementary variables or reshape foundational assumptions, many ultimately align with conventional formulations. This study introduces a novel extension termed residual cumulative generalized exponential entropy to broaden the scope of residual cumulative entropy for continuous distributions. Key attributes of the proposed measure include non-negativity, bounds, its relationship to the continuous entropy measure, and stochastic comparisons. Practical implementations are demonstrated through case studies involving established probability models. Additionally, insights into order statistics are derived to characterize the measure's theoretical underpinnings. The residual cumulative generalized exponential entropy framework bridges concepts such as Bayesian risk assessment and excess wealth ordering. For empirical implementation, non-parametric estimation strategies are devised using data-driven approximations of residual cumulative generalized exponential entropy, with two distinct estimators of the cumulative distribution function evaluated. A practical application is showcased, using clinical diabetes data. The study further explores the role of generalized exponential entropy in identifying distributional symmetry, mainly through its application to uniform distributions to pinpoint symmetry thresholds in ordered data. Finally, the utility of generalized exponential entropy is examined in pattern analysis, with a diabetes dataset serving as a benchmark for evaluating its classification performance.
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spelling doaj-art-3bb13d6c629d49f99b338e7c42c6ec152025-08-20T02:26:19ZengAIMS PressElectronic Research Archive2688-15942025-03-013331633166610.3934/era.2025077On residual cumulative generalized exponential entropy and its application in human healthHanan H. Sakr0Mohamed S. Mohamed1Department of Management Information Systems, College of Business Administration in Hawtat Bani Tamim, Prince Sattam Bin Abdulaziz University, Saudi ArabiaDepartment of Mathematics, Faculty of Education, Ain Shams University, Cairo 11341, EgyptNumerous adaptations of traditional entropy concepts and their residual counterparts have emerged in statistical research. While some methodologies incorporate supplementary variables or reshape foundational assumptions, many ultimately align with conventional formulations. This study introduces a novel extension termed residual cumulative generalized exponential entropy to broaden the scope of residual cumulative entropy for continuous distributions. Key attributes of the proposed measure include non-negativity, bounds, its relationship to the continuous entropy measure, and stochastic comparisons. Practical implementations are demonstrated through case studies involving established probability models. Additionally, insights into order statistics are derived to characterize the measure's theoretical underpinnings. The residual cumulative generalized exponential entropy framework bridges concepts such as Bayesian risk assessment and excess wealth ordering. For empirical implementation, non-parametric estimation strategies are devised using data-driven approximations of residual cumulative generalized exponential entropy, with two distinct estimators of the cumulative distribution function evaluated. A practical application is showcased, using clinical diabetes data. The study further explores the role of generalized exponential entropy in identifying distributional symmetry, mainly through its application to uniform distributions to pinpoint symmetry thresholds in ordered data. Finally, the utility of generalized exponential entropy is examined in pattern analysis, with a diabetes dataset serving as a benchmark for evaluating its classification performance.https://www.aimspress.com/article/doi/10.3934/era.2025077exponential entropyorder statisticsnon-parametric estimationresidual cumulative entropypattern recognitionstochastic order
spellingShingle Hanan H. Sakr
Mohamed S. Mohamed
On residual cumulative generalized exponential entropy and its application in human health
Electronic Research Archive
exponential entropy
order statistics
non-parametric estimation
residual cumulative entropy
pattern recognition
stochastic order
title On residual cumulative generalized exponential entropy and its application in human health
title_full On residual cumulative generalized exponential entropy and its application in human health
title_fullStr On residual cumulative generalized exponential entropy and its application in human health
title_full_unstemmed On residual cumulative generalized exponential entropy and its application in human health
title_short On residual cumulative generalized exponential entropy and its application in human health
title_sort on residual cumulative generalized exponential entropy and its application in human health
topic exponential entropy
order statistics
non-parametric estimation
residual cumulative entropy
pattern recognition
stochastic order
url https://www.aimspress.com/article/doi/10.3934/era.2025077
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