A Generalization of Exponentiated Pareto-I Distribution with Applications

Hussein  Eledum; S.I.  Ansari

doi:10.6180/jase.202405_27(5).0004

A Generalization of Exponentiated Pareto-I Distribution with Applications

Hussein Eledum^1,2This email address is being protected from spambots. You need JavaScript enabled to view it., S.I. Ansari³

¹Department of Statistics, Faculty of Science, University of Tabuk, KSA

²Department of Applied Statistics, Shendi University, Sudan

³Department of Business Administration, Azad Institute of Engineering and Technology, Lucknow, India

Received: June 24, 2023
Accepted: September 9, 2023
Publication Date: September 29, 2023

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202405_27(5).0004

According to earlier research, transmuting a standard distribution often results in a compound distribution that performs better and is more flexible. In light of this fact, this article proposes two novel generalized versions of the exponentiated Pareto-I distribution, called cubic transmuted exponentiated Pareto-I and fourth rank transmuted exponentiated Pareto-I by using the generalization formula for transmuted distribution. Some statistical properties are derived. Model parameters are estimated using the maximum likelihood method. Finally, an application of the two proposed distributions to two real data sets with diverse shapes is illustrated and compared with some distributions based on the exponential family and the exponentiated Pareto-I distribution. The applications suggest that the fourth version performs better than the cubic one for all shapes of the distribution. The new exponentiated Pareto-I models exhibit constant, upside-down, and bathtub hazard rates. The justification for the practicality of the new lifetime models is based on their ability to model real-life data sets from different perspectives.

Keywords: Pareto Distribution; Cubic Transmutation; Fourth Rank Transmutation; Maximum likelihood Estimation; Moment Generating Function

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