Inference for the log-logistic distribution based on an adaptive progressive type-II censoring scheme
<p dir="ltr">The primary aim of this study is to explore and investigate the maximum likelihood (ML) estimation and the Bayesian approach to estimating the parameters of log-logistic distribution and to calculate the approximate intervals for the parameters and the survival function...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| منشور في: |
2019
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513520704421888 |
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| author | Maha F. Sewailem (18147604) |
| author2 | Ayman Baklizi (14158944) |
| author2_role | author |
| author_facet | Maha F. Sewailem (18147604) Ayman Baklizi (14158944) |
| author_role | author |
| dc.creator.none.fl_str_mv | Maha F. Sewailem (18147604) Ayman Baklizi (14158944) |
| dc.date.none.fl_str_mv | 2019-11-11T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1080/25742558.2019.1684228 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Inference_for_the_log-logistic_distribution_based_on_an_adaptive_progressive_type-II_censoring_scheme/25397608 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Cardiovascular medicine and haematology Maximum Likelihood (ML) Bayesian estimation adaptive progressive type-II censoring scheme Squared Error Loss Function (SELF) |
| dc.title.none.fl_str_mv | Inference for the log-logistic distribution based on an adaptive progressive type-II censoring scheme |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The primary aim of this study is to explore and investigate the maximum likelihood (ML) estimation and the Bayesian approach to estimating the parameters of log-logistic distribution and to calculate the approximate intervals for the parameters and the survival function based on adaptive progressive type-II censored data. The ML estimators of the parameters of the probability distribution were obtained via the Newton–Raphson Method. The approximate confidence intervals for the reliability function were calculated using the delta method. The Bayes estimators based on squared error loss function (SELF) and the approximate credible intervals for the unknown parameters and the survival function using the Bayesian approach were constructed using the Markov Chain Monte Carlo (MCMC) method. A Monte Carlo study was performed to examine the proposed methods under different situations, based on mean-squared error, bias, coverage probability, and expected length-estimated criteria. The Bayesian approach appears to be better than the likelihood for estimating the log-logistic model parameters. An application to real data was included.</p><h2>Other Information</h2><p dir="ltr">Published in: Cogent Mathematics & Statistics<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1080/25742558.2019.1684228" target="_blank">https://dx.doi.org/10.1080/25742558.2019.1684228</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_bf0826cc5699fca32879639fcfbbfef3 |
| identifier_str_mv | 10.1080/25742558.2019.1684228 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25397608 |
| publishDate | 2019 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Inference for the log-logistic distribution based on an adaptive progressive type-II censoring schemeMaha F. Sewailem (18147604)Ayman Baklizi (14158944)Biomedical and clinical sciencesCardiovascular medicine and haematologyMaximum Likelihood (ML)Bayesian estimationadaptive progressive type-II censoring schemeSquared Error Loss Function (SELF)<p dir="ltr">The primary aim of this study is to explore and investigate the maximum likelihood (ML) estimation and the Bayesian approach to estimating the parameters of log-logistic distribution and to calculate the approximate intervals for the parameters and the survival function based on adaptive progressive type-II censored data. The ML estimators of the parameters of the probability distribution were obtained via the Newton–Raphson Method. The approximate confidence intervals for the reliability function were calculated using the delta method. The Bayes estimators based on squared error loss function (SELF) and the approximate credible intervals for the unknown parameters and the survival function using the Bayesian approach were constructed using the Markov Chain Monte Carlo (MCMC) method. A Monte Carlo study was performed to examine the proposed methods under different situations, based on mean-squared error, bias, coverage probability, and expected length-estimated criteria. The Bayesian approach appears to be better than the likelihood for estimating the log-logistic model parameters. An application to real data was included.</p><h2>Other Information</h2><p dir="ltr">Published in: Cogent Mathematics & Statistics<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1080/25742558.2019.1684228" target="_blank">https://dx.doi.org/10.1080/25742558.2019.1684228</a></p>2019-11-11T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1080/25742558.2019.1684228https://figshare.com/articles/journal_contribution/Inference_for_the_log-logistic_distribution_based_on_an_adaptive_progressive_type-II_censoring_scheme/25397608CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/253976082019-11-11T03:00:00Z |
| spellingShingle | Inference for the log-logistic distribution based on an adaptive progressive type-II censoring scheme Maha F. Sewailem (18147604) Biomedical and clinical sciences Cardiovascular medicine and haematology Maximum Likelihood (ML) Bayesian estimation adaptive progressive type-II censoring scheme Squared Error Loss Function (SELF) |
| status_str | publishedVersion |
| title | Inference for the log-logistic distribution based on an adaptive progressive type-II censoring scheme |
| title_full | Inference for the log-logistic distribution based on an adaptive progressive type-II censoring scheme |
| title_fullStr | Inference for the log-logistic distribution based on an adaptive progressive type-II censoring scheme |
| title_full_unstemmed | Inference for the log-logistic distribution based on an adaptive progressive type-II censoring scheme |
| title_short | Inference for the log-logistic distribution based on an adaptive progressive type-II censoring scheme |
| title_sort | Inference for the log-logistic distribution based on an adaptive progressive type-II censoring scheme |
| topic | Biomedical and clinical sciences Cardiovascular medicine and haematology Maximum Likelihood (ML) Bayesian estimation adaptive progressive type-II censoring scheme Squared Error Loss Function (SELF) |