Open Access

ARTICLE

Quantile Version of Mathai-Haubold Entropy of Order Statistics

Ibrahim M. Almanjahie^1,2,*, 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 the Special Issue: Intelligent Computing for Engineering Applications)

Computer Modeling in Engineering & Sciences 2021, 128(3), 907-925. https://doi.org/10.32604/cmes.2021.014896

Received 06 November 2020; Accepted 18 March 2021; Issue published 11 August 2021

Abstract

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.

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Citations