The Log Exponential-Power Distribution: Properties, Estimations and Quantile Regression Model

Recently, bounded distributions have attracted attention. These distributions are frequently used in modeling rate and proportion data sets. In this study, a new alternative model is proposed for modeling bounded data sets. Parameter estimations of the proposed distribution are obtained via maximum...

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Autores principales:	Mustafa Ç. Korkmaz, Emrah Altun, Morad Alizadeh, M. El-Morshedy
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	exponential-power distribution point estimation quantile regression residuals unit exponential-power distribution Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/35a1c23dba29444faf14f1da3123a426
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spelling	oai:doaj.org-article:35a1c23dba29444faf14f1da3123a4262021-11-11T18:13:25ZThe Log Exponential-Power Distribution: Properties, Estimations and Quantile Regression Model10.3390/math92126342227-7390https://doaj.org/article/35a1c23dba29444faf14f1da3123a4262021-10-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2634https://doaj.org/toc/2227-7390Recently, bounded distributions have attracted attention. These distributions are frequently used in modeling rate and proportion data sets. In this study, a new alternative model is proposed for modeling bounded data sets. Parameter estimations of the proposed distribution are obtained via maximum likelihood method. In addition, a new regression model is defined under the proposed distribution and its residual analysis is examined. As a result of the empirical studies on real data sets, it is observed that the proposed regression model gives better results than the unit-Weibull and Kumaraswamy regression models.Mustafa Ç. KorkmazEmrah AltunMorad AlizadehM. El-MorshedyMDPI AGarticleexponential-power distributionpoint estimationquantile regressionresidualsunit exponential-power distributionMathematicsQA1-939ENMathematics, Vol 9, Iss 2634, p 2634 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	exponential-power distribution point estimation quantile regression residuals unit exponential-power distribution Mathematics QA1-939
spellingShingle	exponential-power distribution point estimation quantile regression residuals unit exponential-power distribution Mathematics QA1-939 Mustafa Ç. Korkmaz Emrah Altun Morad Alizadeh M. El-Morshedy The Log Exponential-Power Distribution: Properties, Estimations and Quantile Regression Model
description	Recently, bounded distributions have attracted attention. These distributions are frequently used in modeling rate and proportion data sets. In this study, a new alternative model is proposed for modeling bounded data sets. Parameter estimations of the proposed distribution are obtained via maximum likelihood method. In addition, a new regression model is defined under the proposed distribution and its residual analysis is examined. As a result of the empirical studies on real data sets, it is observed that the proposed regression model gives better results than the unit-Weibull and Kumaraswamy regression models.
format	article
author	Mustafa Ç. Korkmaz Emrah Altun Morad Alizadeh M. El-Morshedy
author_facet	Mustafa Ç. Korkmaz Emrah Altun Morad Alizadeh M. El-Morshedy
author_sort	Mustafa Ç. Korkmaz
title	The Log Exponential-Power Distribution: Properties, Estimations and Quantile Regression Model
title_short	The Log Exponential-Power Distribution: Properties, Estimations and Quantile Regression Model
title_full	The Log Exponential-Power Distribution: Properties, Estimations and Quantile Regression Model
title_fullStr	The Log Exponential-Power Distribution: Properties, Estimations and Quantile Regression Model
title_full_unstemmed	The Log Exponential-Power Distribution: Properties, Estimations and Quantile Regression Model
title_sort	log exponential-power distribution: properties, estimations and quantile regression model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/35a1c23dba29444faf14f1da3123a426
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The Log Exponential-Power Distribution: Properties, Estimations and Quantile Regression Model

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