Estimation of Power Lomax Distribution for Censored Data With Applications

In this study, power Lomax (PL) distribution parameters are estimated under an adaptive Type-II progressive censoring scheme, utilizing both frequentist and Bayesian statistical estimations. The model parameters, reliability and hazard functions, and coefficient of variation are all determined using...

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Main Authors: Abdelfattah Mustafa, Samah M. Ahmed
Format: Article
Language:English
Published: Wiley 2025-01-01
Series:Journal of Mathematics
Online Access:http://dx.doi.org/10.1155/jom/3682098
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author Abdelfattah Mustafa
Samah M. Ahmed
author_facet Abdelfattah Mustafa
Samah M. Ahmed
author_sort Abdelfattah Mustafa
collection DOAJ
description In this study, power Lomax (PL) distribution parameters are estimated under an adaptive Type-II progressive censoring scheme, utilizing both frequentist and Bayesian statistical estimations. The model parameters, reliability and hazard functions, and coefficient of variation are all determined using an iterative procedure in the frequentist estimation. Furthermore, the asymptotic normality features of maximum likelihood estimates (MLEs) are used to calculate MLEs and asymptotic confidence intervals. The Bayesian method estimates under both symmetric and asymmetric loss functions by using the Markov Chain Monte Carlo (MCMC) technique. The performance of the Bayesian estimates and the MLEs is compared and contrasted in a simulated study. Finally, a numerical analysis of a real data set is presented to illustrate the application of the proposed inferential processes.
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spelling doaj-art-a3a4bd2a1b45407fb6399b00f325493c2025-08-20T03:28:06ZengWileyJournal of Mathematics2314-47852025-01-01202510.1155/jom/3682098Estimation of Power Lomax Distribution for Censored Data With ApplicationsAbdelfattah Mustafa0Samah M. Ahmed1Department of MathematicsDepartment of MathematicsIn this study, power Lomax (PL) distribution parameters are estimated under an adaptive Type-II progressive censoring scheme, utilizing both frequentist and Bayesian statistical estimations. The model parameters, reliability and hazard functions, and coefficient of variation are all determined using an iterative procedure in the frequentist estimation. Furthermore, the asymptotic normality features of maximum likelihood estimates (MLEs) are used to calculate MLEs and asymptotic confidence intervals. The Bayesian method estimates under both symmetric and asymmetric loss functions by using the Markov Chain Monte Carlo (MCMC) technique. The performance of the Bayesian estimates and the MLEs is compared and contrasted in a simulated study. Finally, a numerical analysis of a real data set is presented to illustrate the application of the proposed inferential processes.http://dx.doi.org/10.1155/jom/3682098
spellingShingle Abdelfattah Mustafa
Samah M. Ahmed
Estimation of Power Lomax Distribution for Censored Data With Applications
Journal of Mathematics
title Estimation of Power Lomax Distribution for Censored Data With Applications
title_full Estimation of Power Lomax Distribution for Censored Data With Applications
title_fullStr Estimation of Power Lomax Distribution for Censored Data With Applications
title_full_unstemmed Estimation of Power Lomax Distribution for Censored Data With Applications
title_short Estimation of Power Lomax Distribution for Censored Data With Applications
title_sort estimation of power lomax distribution for censored data with applications
url http://dx.doi.org/10.1155/jom/3682098
work_keys_str_mv AT abdelfattahmustafa estimationofpowerlomaxdistributionforcensoreddatawithapplications
AT samahmahmed estimationofpowerlomaxdistributionforcensoreddatawithapplications