Computational Analysis of Novel Extended Lindley Progressively Censored Data

作     者：Refah Alotaibi Mazen Nassar Ahmed Elshahhat 

作者机构：Department of Mathematical SciencesCollege of SciencePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of StatisticsFaculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of StatisticsFaculty of CommerceZagazig UniversityZagazigEgypt Faculty of Technology and DevelopmentZagazig UniversityZagazig44519Egypt 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2024年第138卷第3期

页      面：2571-2596页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R50) Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia 

主　　题：Marshall-Olkin-Lindleymodel reliability inference Bayesian and classical inference progressive Type-II censoring 

摘      要：A novel extended Lindley lifetime model that exhibits unimodal or decreasing density shapes as well as increasing,bathtub or unimodal-then-bathtub failure rates, named the Marshall-Olkin-Lindley (MOL) model is *** this research, using a progressive Type-II censored, various inferences of the MOL model parameters oflife are introduced. Utilizing the maximum likelihood method as a classical approach, the estimators of themodel parameters and various reliability measures are investigated. Against both symmetric and asymmetric lossfunctions, the Bayesian estimates are obtained using the Markov Chain Monte Carlo (MCMC) technique with theassumption of independent gamma priors. From the Fisher information data and the simulatedMarkovian chains,the approximate asymptotic interval and the highest posterior density interval, respectively, of each unknownparameter are calculated. Via an extensive simulated study, the usefulness of the various suggested strategies isassessedwith respect to some evaluationmetrics such as mean squared errors, mean relative absolute biases, averageconfidence lengths, and coverage percentages. Comparing the Bayesian estimations based on the asymmetric lossfunction to the traditional technique or the symmetric loss function-based Bayesian estimations, the analysisdemonstrates that asymmetric loss function-based Bayesian estimations are preferred. Finally, two data sets,representing vinyl chloride and repairable mechanical equipment items, have been investigated to support theapproaches proposed and show the superiority of the proposed model compared to the other fourteen lifetimemodels.