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Research Article | Open Access

Some properties on dynamic cumulative Tsallis residual entropy measures based on Sarmanov family with applications to motor data

M. A. Alawady1,2H. M. Barakat2Asamh Saleh M. Al Luhayb3( )G. M. Mansour2
Department of Statistics and Operations Research, College of Science, Qassim University, Buraydah 51482, Saudi Arabia
Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
Department of Mathematics, College of Science, Qassim University, P. O. Box 6644, Buraydah 51452, Saudi Arabia
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Abstract

This study presents Tsallis and Rényi entropies as continuous measures of information for continuous distributions based on concomitants of generalized order statistics from the Sarmanov (SAR) family. Additionally, the characteristics and their relationship to other information measures are presented. One of such measures is the cumulative Tsallis residual entropy (CTRE), which can be regarded as an alternative measure of dispersion, and we study its dynamic version. Moreover, applications of these results are given for order statistics, and the record values as special cases with uniform, Weibull, and exponential marginal distributions. Furthermore, the empirical alternative CTRE (denoted ACTRE) was proposed to estimate these information measures. Finally, a real-world dataset has been examined for illustrative purposes and demonstrates superior goodness-of-fit and interpretability compared with classical bivariate distributions.

CLC number: 60B12, 62G30

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AIMS Mathematics
Pages 8271-8307

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Cite this article:
Alawady MA, Barakat HM, Luhayb ASMA, et al. Some properties on dynamic cumulative Tsallis residual entropy measures based on Sarmanov family with applications to motor data. AIMS Mathematics, 2026, 11(3): 8271-8307. https://doi.org/10.3934/math.2026340

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Received: 17 January 2026
Revised: 05 March 2026
Accepted: 11 March 2026
Published: 15 March 2026
©2026 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)