Classical and Bayesian Estimation of the Inverse Weibull Distribution: Using Progressive Type-I Censoring Scheme
The challenge of estimating the parameters for the inverse Weibull (IW) distribution employing progressive censoring Type-I (PCTI) will be addressed in this study using Bayesian and non-Bayesian procedures. To address the issue of censoring time selection, qauntiles from the IW lifetime distribution...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/5701529 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850167053919453184 |
|---|---|
| author | Ali Algarni Mohammed Elgarhy Abdullah M Almarashi Aisha Fayomi Ahmed R El-Saeed |
| author_facet | Ali Algarni Mohammed Elgarhy Abdullah M Almarashi Aisha Fayomi Ahmed R El-Saeed |
| author_sort | Ali Algarni |
| collection | DOAJ |
| description | The challenge of estimating the parameters for the inverse Weibull (IW) distribution employing progressive censoring Type-I (PCTI) will be addressed in this study using Bayesian and non-Bayesian procedures. To address the issue of censoring time selection, qauntiles from the IW lifetime distribution will be implemented as censoring time points for PCTI. Focusing on the censoring schemes, maximum likelihood estimators (MLEs) and asymptotic confidence intervals (ACI) for unknown parameters are constructed. Under the squared error (SEr) loss function, Bayes estimates (BEs) and concomitant maximum posterior density credible interval estimations are also produced. The BEs are assessed using two methods: Lindley’s approximation (LiA) technique and the Metropolis-Hasting (MH) algorithm utilizing Markov Chain Monte Carlo (MCMC). The theoretical implications of MLEs and BEs for specified schemes of PCTI samples are shown via a simulation study to compare the performance of the different suggested estimators. Finally, application of two real data sets will be employed. |
| format | Article |
| id | doaj-art-db3510a8f3844f98a85e958d66f76126 |
| institution | OA Journals |
| issn | 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-db3510a8f3844f98a85e958d66f761262025-08-20T02:21:17ZengWileyAdvances in Civil Engineering1687-80942021-01-01202110.1155/2021/5701529Classical and Bayesian Estimation of the Inverse Weibull Distribution: Using Progressive Type-I Censoring SchemeAli Algarni0Mohammed Elgarhy1Abdullah M Almarashi2Aisha Fayomi3Ahmed R El-Saeed4Statistics DepartmentThe Higher Institute of Commercial SciencesStatistics DepartmentStatistics DepartmentDepartment of Basic SciencesThe challenge of estimating the parameters for the inverse Weibull (IW) distribution employing progressive censoring Type-I (PCTI) will be addressed in this study using Bayesian and non-Bayesian procedures. To address the issue of censoring time selection, qauntiles from the IW lifetime distribution will be implemented as censoring time points for PCTI. Focusing on the censoring schemes, maximum likelihood estimators (MLEs) and asymptotic confidence intervals (ACI) for unknown parameters are constructed. Under the squared error (SEr) loss function, Bayes estimates (BEs) and concomitant maximum posterior density credible interval estimations are also produced. The BEs are assessed using two methods: Lindley’s approximation (LiA) technique and the Metropolis-Hasting (MH) algorithm utilizing Markov Chain Monte Carlo (MCMC). The theoretical implications of MLEs and BEs for specified schemes of PCTI samples are shown via a simulation study to compare the performance of the different suggested estimators. Finally, application of two real data sets will be employed.http://dx.doi.org/10.1155/2021/5701529 |
| spellingShingle | Ali Algarni Mohammed Elgarhy Abdullah M Almarashi Aisha Fayomi Ahmed R El-Saeed Classical and Bayesian Estimation of the Inverse Weibull Distribution: Using Progressive Type-I Censoring Scheme Advances in Civil Engineering |
| title | Classical and Bayesian Estimation of the Inverse Weibull Distribution: Using Progressive Type-I Censoring Scheme |
| title_full | Classical and Bayesian Estimation of the Inverse Weibull Distribution: Using Progressive Type-I Censoring Scheme |
| title_fullStr | Classical and Bayesian Estimation of the Inverse Weibull Distribution: Using Progressive Type-I Censoring Scheme |
| title_full_unstemmed | Classical and Bayesian Estimation of the Inverse Weibull Distribution: Using Progressive Type-I Censoring Scheme |
| title_short | Classical and Bayesian Estimation of the Inverse Weibull Distribution: Using Progressive Type-I Censoring Scheme |
| title_sort | classical and bayesian estimation of the inverse weibull distribution using progressive type i censoring scheme |
| url | http://dx.doi.org/10.1155/2021/5701529 |
| work_keys_str_mv | AT alialgarni classicalandbayesianestimationoftheinverseweibulldistributionusingprogressivetypeicensoringscheme AT mohammedelgarhy classicalandbayesianestimationoftheinverseweibulldistributionusingprogressivetypeicensoringscheme AT abdullahmalmarashi classicalandbayesianestimationoftheinverseweibulldistributionusingprogressivetypeicensoringscheme AT aishafayomi classicalandbayesianestimationoftheinverseweibulldistributionusingprogressivetypeicensoringscheme AT ahmedrelsaeed classicalandbayesianestimationoftheinverseweibulldistributionusingprogressivetypeicensoringscheme |