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Quantile Version of Mathai-Haubold Entropy of Order Statistics

Ibrahim M. Almanjahie1,2,*, Javid Gani Dar3, Amer Ibrahim Al-Omari4, Aijaz Mir5

1 Department of Mathematics, College of Science, King Khalid University, Abha, 62529, Saudi Arabia
2 Statistical Research and Studies Support Unit, King Khalid University, Abha, 62529, Saudi Arabia
3 Department of Mathematical Sciences, IUST, Kashmir, 192231, India
4 Department of Mathematics, Faculty of Science, Al al-Bayt University, Mafraq, 25113, Jordan
5 Department of Mathematics, Govt. Degree College Kilam, Higher Education, J & K, 192231, India

* Corresponding Author: Ibrahim M. Almanjahie. Email:

(This article belongs to this Special Issue: Intelligent Computing for Engineering Applications)

Computer Modeling in Engineering & Sciences 2021, 128(3), 907-925.


Many researchers measure the uncertainty of a random variable using quantile-based entropy techniques. These techniques are useful in engineering applications and have some exceptional characteristics than their distribution function method. Considering order statistics, the key focus of this article is to propose new quantile-based Mathai-Haubold entropy and investigate its characteristics. The divergence measure of the Mathai-Haubold is also considered and some of its properties are established. Further, based on order statistics, we propose the residual entropy of the quantile-based Mathai-Haubold and some of its property results are proved. The performance of the proposed quantile-based Mathai-Haubold entropy is investigated by simulation studies. Finally, a real data application is used to compare our proposed quantile-based entropy to the existing quantile entropies. The results reveal the outperformance of our proposed entropy to the other entropies.


Cite This Article

Almanjahie, I. M., Dar, J. G., Al-Omari, A. I., Mir, A. (2021). Quantile Version of Mathai-Haubold Entropy of Order Statistics. CMES-Computer Modeling in Engineering & Sciences, 128(3), 907–925.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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