Bayesian estimation for Dagum distribution based on progressive type I interval censoring.

In this paper, we consider Dagum distribution which is capable of modeling various shapes of failure rates and aging criteria. Based on progressively type-I interval censoring data, we first obtain the maximum likelihood estimators and the approximate confidence intervals of the unknown parameters o...

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Autores principales:	Refah Alotaibi, Hoda Rezk, Sanku Dey, Hassan Okasha
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b48c83ddf88f48538b4596662a5f7cf3
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spelling	oai:doaj.org-article:b48c83ddf88f48538b4596662a5f7cf32021-12-02T20:07:21ZBayesian estimation for Dagum distribution based on progressive type I interval censoring.1932-620310.1371/journal.pone.0252556https://doaj.org/article/b48c83ddf88f48538b4596662a5f7cf32021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252556https://doaj.org/toc/1932-6203In this paper, we consider Dagum distribution which is capable of modeling various shapes of failure rates and aging criteria. Based on progressively type-I interval censoring data, we first obtain the maximum likelihood estimators and the approximate confidence intervals of the unknown parameters of the Dagum distribution. Next, we obtain the Bayes estimators of the parameters of Dagum distribution under the squared error loss (SEL) and balanced squared error loss (BSEL) functions using independent informative gamma and non informative uniform priors for both scale and two shape parameters. A Monte Carlo simulation study is performed to assess the performance of the proposed Bayes estimators with the maximum likelihood estimators. We also compute credible intervals and symmetric 100(1 - τ)% two-sided Bayes probability intervals under the respective approaches. Besides, based on observed samples, Bayes predictive estimates and intervals are obtained using one-and two-sample schemes. Simulation results reveal that the Bayes estimates based on SEL and BSEL performs better than maximum likelihood estimates in terms of bias and MSEs. Besides, credible intervals have smaller interval lengths than confidence interval. Further, predictive estimates based on SEL with informative prior performs better than non-informative prior for both one and two sample schemes. Further, the optimal censoring scheme has been suggested using a optimality criteria. Finally, we analyze a data set to illustrate the results derived.Refah AlotaibiHoda RezkSanku DeyHassan OkashaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252556 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Refah Alotaibi Hoda Rezk Sanku Dey Hassan Okasha Bayesian estimation for Dagum distribution based on progressive type I interval censoring.
description	In this paper, we consider Dagum distribution which is capable of modeling various shapes of failure rates and aging criteria. Based on progressively type-I interval censoring data, we first obtain the maximum likelihood estimators and the approximate confidence intervals of the unknown parameters of the Dagum distribution. Next, we obtain the Bayes estimators of the parameters of Dagum distribution under the squared error loss (SEL) and balanced squared error loss (BSEL) functions using independent informative gamma and non informative uniform priors for both scale and two shape parameters. A Monte Carlo simulation study is performed to assess the performance of the proposed Bayes estimators with the maximum likelihood estimators. We also compute credible intervals and symmetric 100(1 - τ)% two-sided Bayes probability intervals under the respective approaches. Besides, based on observed samples, Bayes predictive estimates and intervals are obtained using one-and two-sample schemes. Simulation results reveal that the Bayes estimates based on SEL and BSEL performs better than maximum likelihood estimates in terms of bias and MSEs. Besides, credible intervals have smaller interval lengths than confidence interval. Further, predictive estimates based on SEL with informative prior performs better than non-informative prior for both one and two sample schemes. Further, the optimal censoring scheme has been suggested using a optimality criteria. Finally, we analyze a data set to illustrate the results derived.
format	article
author	Refah Alotaibi Hoda Rezk Sanku Dey Hassan Okasha
author_facet	Refah Alotaibi Hoda Rezk Sanku Dey Hassan Okasha
author_sort	Refah Alotaibi
title	Bayesian estimation for Dagum distribution based on progressive type I interval censoring.
title_short	Bayesian estimation for Dagum distribution based on progressive type I interval censoring.
title_full	Bayesian estimation for Dagum distribution based on progressive type I interval censoring.
title_fullStr	Bayesian estimation for Dagum distribution based on progressive type I interval censoring.
title_full_unstemmed	Bayesian estimation for Dagum distribution based on progressive type I interval censoring.
title_sort	bayesian estimation for dagum distribution based on progressive type i interval censoring.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/b48c83ddf88f48538b4596662a5f7cf3
work_keys_str_mv	AT refahalotaibi bayesianestimationfordagumdistributionbasedonprogressivetypeiintervalcensoring AT hodarezk bayesianestimationfordagumdistributionbasedonprogressivetypeiintervalcensoring AT sankudey bayesianestimationfordagumdistributionbasedonprogressivetypeiintervalcensoring AT hassanokasha bayesianestimationfordagumdistributionbasedonprogressivetypeiintervalcensoring
_version_	1718375324766437376

Bayesian estimation for Dagum distribution based on progressive type I interval censoring.

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