Bayesian Estimation and Prediction of Inverse Power Lomax Model Under Censored Data With Applications

This study presents a comparative analysis of frequentist and Bayesian estimation techniques for the parameters of the inverse power Lomax distribution, employing an adaptive Type-II progressive censoring approach. The maximum likelihood estimations (MLEs) and their corresponding asymptotic confiden...

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Main Authors: Samah M. Ahmed, M. I. Khan, Abdelfattah Mustafa
Format: Article
Language:English
Published: Wiley 2025-01-01
Series:Journal of Mathematics
Online Access:http://dx.doi.org/10.1155/jom/7285331
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author Samah M. Ahmed
M. I. Khan
Abdelfattah Mustafa
author_facet Samah M. Ahmed
M. I. Khan
Abdelfattah Mustafa
author_sort Samah M. Ahmed
collection DOAJ
description This study presents a comparative analysis of frequentist and Bayesian estimation techniques for the parameters of the inverse power Lomax distribution, employing an adaptive Type-II progressive censoring approach. The maximum likelihood estimations (MLEs) and their corresponding asymptotic confidence intervals are derived. Bayesian estimation is carried out via the Markov chain Monte Carlo (MCMC) method, considering both symmetric and asymmetric loss functions. A simulation study is used to assess and compare the performance of the Bayes estimates and MLEs. The study also investigates Bayesian prediction of order statistics. The proposed inferential procedures are then validated using a numerical study based on a real dataset.
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issn 2314-4785
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spelling doaj-art-eeca711f240b4d8598a0d67b0986d55e2025-08-20T03:56:50ZengWileyJournal of Mathematics2314-47852025-01-01202510.1155/jom/7285331Bayesian Estimation and Prediction of Inverse Power Lomax Model Under Censored Data With ApplicationsSamah M. Ahmed0M. I. Khan1Abdelfattah Mustafa2Mathematics DepartmentMathematics DepartmentMathematics DepartmentThis study presents a comparative analysis of frequentist and Bayesian estimation techniques for the parameters of the inverse power Lomax distribution, employing an adaptive Type-II progressive censoring approach. The maximum likelihood estimations (MLEs) and their corresponding asymptotic confidence intervals are derived. Bayesian estimation is carried out via the Markov chain Monte Carlo (MCMC) method, considering both symmetric and asymmetric loss functions. A simulation study is used to assess and compare the performance of the Bayes estimates and MLEs. The study also investigates Bayesian prediction of order statistics. The proposed inferential procedures are then validated using a numerical study based on a real dataset.http://dx.doi.org/10.1155/jom/7285331
spellingShingle Samah M. Ahmed
M. I. Khan
Abdelfattah Mustafa
Bayesian Estimation and Prediction of Inverse Power Lomax Model Under Censored Data With Applications
Journal of Mathematics
title Bayesian Estimation and Prediction of Inverse Power Lomax Model Under Censored Data With Applications
title_full Bayesian Estimation and Prediction of Inverse Power Lomax Model Under Censored Data With Applications
title_fullStr Bayesian Estimation and Prediction of Inverse Power Lomax Model Under Censored Data With Applications
title_full_unstemmed Bayesian Estimation and Prediction of Inverse Power Lomax Model Under Censored Data With Applications
title_short Bayesian Estimation and Prediction of Inverse Power Lomax Model Under Censored Data With Applications
title_sort bayesian estimation and prediction of inverse power lomax model under censored data with applications
url http://dx.doi.org/10.1155/jom/7285331
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AT mikhan bayesianestimationandpredictionofinversepowerlomaxmodelundercensoreddatawithapplications
AT abdelfattahmustafa bayesianestimationandpredictionofinversepowerlomaxmodelundercensoreddatawithapplications