A New Generator of Probability Models: The Exponentiated Sine-G Family for Lifetime Studies

In this article, we propose the exponentiated sine-generated family of distributions. Some important properties are demonstrated, such as the series representation of the probability density function, quantile function, moments, stress-strength reliability, and Rényi entropy. A particular member, ca...

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Autores principales:	Mustapha Muhammad, Huda M. Alshanbari, Ayed R. A. Alanzi, Lixia Liu, Waqas Sami, Christophe Chesneau, Farrukh Jamal
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	sine-generated family Weibull distribution quantile entropy parametric estimation Bayes estimation Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/0abb26cc3be6436c8a4c8a3b6af02de1
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spelling	oai:doaj.org-article:0abb26cc3be6436c8a4c8a3b6af02de12021-11-25T17:29:15ZA New Generator of Probability Models: The Exponentiated Sine-G Family for Lifetime Studies10.3390/e231113941099-4300https://doaj.org/article/0abb26cc3be6436c8a4c8a3b6af02de12021-10-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1394https://doaj.org/toc/1099-4300In this article, we propose the exponentiated sine-generated family of distributions. Some important properties are demonstrated, such as the series representation of the probability density function, quantile function, moments, stress-strength reliability, and Rényi entropy. A particular member, called the exponentiated sine Weibull distribution, is highlighted; we analyze its skewness and kurtosis, moments, quantile function, residual mean and reversed mean residual life functions, order statistics, and extreme value distributions. Maximum likelihood estimation and Bayes estimation under the square error loss function are considered. Simulation studies are used to assess the techniques, and their performance gives satisfactory results as discussed by the mean square error, confidence intervals, and coverage probabilities of the estimates. The stress-strength reliability parameter of the exponentiated sine Weibull model is derived and estimated by the maximum likelihood estimation method. Also, nonparametric bootstrap techniques are used to approximate the confidence interval of the reliability parameter. A simulation is conducted to examine the mean square error, standard deviations, confidence intervals, and coverage probabilities of the reliability parameter. Finally, three real applications of the exponentiated sine Weibull model are provided. One of them considers stress-strength data.Mustapha MuhammadHuda M. AlshanbariAyed R. A. AlanziLixia LiuWaqas SamiChristophe ChesneauFarrukh JamalMDPI AGarticlesine-generated familyWeibull distributionquantileentropyparametric estimationBayes estimationScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1394, p 1394 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sine-generated family Weibull distribution quantile entropy parametric estimation Bayes estimation Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	sine-generated family Weibull distribution quantile entropy parametric estimation Bayes estimation Science Q Astrophysics QB460-466 Physics QC1-999 Mustapha Muhammad Huda M. Alshanbari Ayed R. A. Alanzi Lixia Liu Waqas Sami Christophe Chesneau Farrukh Jamal A New Generator of Probability Models: The Exponentiated Sine-G Family for Lifetime Studies
description	In this article, we propose the exponentiated sine-generated family of distributions. Some important properties are demonstrated, such as the series representation of the probability density function, quantile function, moments, stress-strength reliability, and Rényi entropy. A particular member, called the exponentiated sine Weibull distribution, is highlighted; we analyze its skewness and kurtosis, moments, quantile function, residual mean and reversed mean residual life functions, order statistics, and extreme value distributions. Maximum likelihood estimation and Bayes estimation under the square error loss function are considered. Simulation studies are used to assess the techniques, and their performance gives satisfactory results as discussed by the mean square error, confidence intervals, and coverage probabilities of the estimates. The stress-strength reliability parameter of the exponentiated sine Weibull model is derived and estimated by the maximum likelihood estimation method. Also, nonparametric bootstrap techniques are used to approximate the confidence interval of the reliability parameter. A simulation is conducted to examine the mean square error, standard deviations, confidence intervals, and coverage probabilities of the reliability parameter. Finally, three real applications of the exponentiated sine Weibull model are provided. One of them considers stress-strength data.
format	article
author	Mustapha Muhammad Huda M. Alshanbari Ayed R. A. Alanzi Lixia Liu Waqas Sami Christophe Chesneau Farrukh Jamal
author_facet	Mustapha Muhammad Huda M. Alshanbari Ayed R. A. Alanzi Lixia Liu Waqas Sami Christophe Chesneau Farrukh Jamal
author_sort	Mustapha Muhammad
title	A New Generator of Probability Models: The Exponentiated Sine-G Family for Lifetime Studies
title_short	A New Generator of Probability Models: The Exponentiated Sine-G Family for Lifetime Studies
title_full	A New Generator of Probability Models: The Exponentiated Sine-G Family for Lifetime Studies
title_fullStr	A New Generator of Probability Models: The Exponentiated Sine-G Family for Lifetime Studies
title_full_unstemmed	A New Generator of Probability Models: The Exponentiated Sine-G Family for Lifetime Studies
title_sort	new generator of probability models: the exponentiated sine-g family for lifetime studies
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0abb26cc3be6436c8a4c8a3b6af02de1
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A New Generator of Probability Models: The Exponentiated Sine-G Family for Lifetime Studies

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