Modeling engineering data using extended power-Lindley distribution: Properties and estimation methods

In this paper, we introduce a new flexible distribution called the Weibull Marshall-Olkin power-Lindley (WMOPL) distribution to extend and increase the flexibility of the power-Lindley distribution to model engineering related data. The WMOPL has the ability to model lifetime data with decreasing, i...

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Autores principales:	Abdulhakim A. Al-Babtain, Devendra Kumar, Ahmed M. Gemeay, Sanku Dey, Ahmed Z. Afify
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Anderson–Darling estimation Maximum likelihood estimation Maximum product of spacing Moments Power-Lindley distribution Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/02e69447c9244108b53aaa56a0b2e344
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spelling	oai:doaj.org-article:02e69447c9244108b53aaa56a0b2e3442021-11-18T04:43:50ZModeling engineering data using extended power-Lindley distribution: Properties and estimation methods1018-364710.1016/j.jksus.2021.101582https://doaj.org/article/02e69447c9244108b53aaa56a0b2e3442021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1018364721002445https://doaj.org/toc/1018-3647In this paper, we introduce a new flexible distribution called the Weibull Marshall-Olkin power-Lindley (WMOPL) distribution to extend and increase the flexibility of the power-Lindley distribution to model engineering related data. The WMOPL has the ability to model lifetime data with decreasing, increasing, J-shaped, reversed-J shaped, unimodal, bathtub, and modified bathtub shaped hazard rates. Various properties of the WMOPL distribution are derived. Seven frequentist estimation methods are considered to estimate the WMOPL parameters. To evaluate the performance of the proposed methods and provide a guideline for engineers and practitioners to choose the best estimation method, a detailed simulation study is carried out. The performance of the estimators have been ranked based on partial and overall ranks. The performance and flexibility of the introduced distribution are studied using one real data set from the field of engineering. The data show that the WMOPL model performs better than some well-known extensions of the power-Lindley and Lindley distributions.Abdulhakim A. Al-BabtainDevendra KumarAhmed M. GemeaySanku DeyAhmed Z. AfifyElsevierarticleAnderson–Darling estimationMaximum likelihood estimationMaximum product of spacingMomentsPower-Lindley distributionScience (General)Q1-390ENJournal of King Saud University: Science, Vol 33, Iss 8, Pp 101582- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Anderson–Darling estimation Maximum likelihood estimation Maximum product of spacing Moments Power-Lindley distribution Science (General) Q1-390
spellingShingle	Anderson–Darling estimation Maximum likelihood estimation Maximum product of spacing Moments Power-Lindley distribution Science (General) Q1-390 Abdulhakim A. Al-Babtain Devendra Kumar Ahmed M. Gemeay Sanku Dey Ahmed Z. Afify Modeling engineering data using extended power-Lindley distribution: Properties and estimation methods
description	In this paper, we introduce a new flexible distribution called the Weibull Marshall-Olkin power-Lindley (WMOPL) distribution to extend and increase the flexibility of the power-Lindley distribution to model engineering related data. The WMOPL has the ability to model lifetime data with decreasing, increasing, J-shaped, reversed-J shaped, unimodal, bathtub, and modified bathtub shaped hazard rates. Various properties of the WMOPL distribution are derived. Seven frequentist estimation methods are considered to estimate the WMOPL parameters. To evaluate the performance of the proposed methods and provide a guideline for engineers and practitioners to choose the best estimation method, a detailed simulation study is carried out. The performance of the estimators have been ranked based on partial and overall ranks. The performance and flexibility of the introduced distribution are studied using one real data set from the field of engineering. The data show that the WMOPL model performs better than some well-known extensions of the power-Lindley and Lindley distributions.
format	article
author	Abdulhakim A. Al-Babtain Devendra Kumar Ahmed M. Gemeay Sanku Dey Ahmed Z. Afify
author_facet	Abdulhakim A. Al-Babtain Devendra Kumar Ahmed M. Gemeay Sanku Dey Ahmed Z. Afify
author_sort	Abdulhakim A. Al-Babtain
title	Modeling engineering data using extended power-Lindley distribution: Properties and estimation methods
title_short	Modeling engineering data using extended power-Lindley distribution: Properties and estimation methods
title_full	Modeling engineering data using extended power-Lindley distribution: Properties and estimation methods
title_fullStr	Modeling engineering data using extended power-Lindley distribution: Properties and estimation methods
title_full_unstemmed	Modeling engineering data using extended power-Lindley distribution: Properties and estimation methods
title_sort	modeling engineering data using extended power-lindley distribution: properties and estimation methods
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/02e69447c9244108b53aaa56a0b2e344
work_keys_str_mv	AT abdulhakimaalbabtain modelingengineeringdatausingextendedpowerlindleydistributionpropertiesandestimationmethods AT devendrakumar modelingengineeringdatausingextendedpowerlindleydistributionpropertiesandestimationmethods AT ahmedmgemeay modelingengineeringdatausingextendedpowerlindleydistributionpropertiesandestimationmethods AT sankudey modelingengineeringdatausingextendedpowerlindleydistributionpropertiesandestimationmethods AT ahmedzafify modelingengineeringdatausingextendedpowerlindleydistributionpropertiesandestimationmethods
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Modeling engineering data using extended power-Lindley distribution: Properties and estimation methods

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