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Inference of Truncated Lomax Inverse Lomax Distribution with Applications

Abdullah Ali H. Ahmadini1, Amal Hassan2, M. Elgarhy3,*, Mahmoud Elsehetry4, Shokrya S. Alshqaq5, Said G. Nassr6

1 Department of Mathematics, Faculty of Science, Jazan University, Jazan, Saudi Arabia
2 Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, 12613, Egypt
3 The Higher Institute of Commercial Sciences, Al Mahalla Al Kubra, 31951, Egypt
4 Deanship of Information Technology, King Abdulaziz University, Jeddah, 21589, Kingdom of Saudi Arabia
5Department of Mathematics, Faculty of Science, Jazan University, Jazan, Saudi Arabia
6 Faculty of Business Administration, Sinai University, Cairo, Egypt

* Corresponding Author: M. Elgarhy. Email:

Intelligent Automation & Soft Computing 2021, 29(1), 199-212.


This paper introduces a modified form of the inverse Lomax distribution which offers more flexibility for modeling lifetime data. The new three-parameter model is provided as a member of the truncated Lomax-G procedure. The new modified distribution is called the truncated Lomax inverse Lomax distribution. The density of the new model can be represented as a linear combination of the inverse Lomax distribution. Expansions for quantile function, moment generating function, probability weighted moments, ordinary moments, incomplete moments, inverse moments, conditional moments, and Rényi entropy measure are investigated. The new distribution is capable of monotonically increasing, decreasing, reversed J-shaped and upside-down shaped hazard rates. Maximum likelihood estimators of the population parameters are derived. Also, the approximate confidence interval of parameters is constructed. A simulation study framework is established to assess the accuracy of estimates through some measures. Simulation outcomes show that there is a great agreement between theoretical and empirical studies. The applicability of the truncated Lomax inverse Lomax model is illustrated through two real lifetime data sets and its goodness-of-fit is compared with that of the recent models. In fact, it provides a better fit to these data than the other competitive models.


Cite This Article

A. Ali H. Ahmadini, A. Hassan, M. Elgarhy, M. Elsehetry, S. S. Alshqaq et al., "Inference of truncated lomax inverse lomax distribution with applications," Intelligent Automation & Soft Computing, vol. 29, no.1, pp. 199–212, 2021.

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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