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A new regression model for bounded response variable: An alternative to the beta and unit-Lindley regression models

A new distribution defined on (0,1) interval is introduced. Its probability density and cumulative distribution functions have simple forms. Thanks to its simple forms, the moments, incomplete moments and quantile function of the proposed distribution are derived and obtained in explicit forms. Four...

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Detalles Bibliográficos
Autores principales: Altun, Emrah, El-Morshedy, M., Eliwa, M. S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7822343/
https://www.ncbi.nlm.nih.gov/pubmed/33481884
http://dx.doi.org/10.1371/journal.pone.0245627
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author Altun, Emrah
El-Morshedy, M.
Eliwa, M. S.
author_facet Altun, Emrah
El-Morshedy, M.
Eliwa, M. S.
author_sort Altun, Emrah
collection PubMed
description A new distribution defined on (0,1) interval is introduced. Its probability density and cumulative distribution functions have simple forms. Thanks to its simple forms, the moments, incomplete moments and quantile function of the proposed distribution are derived and obtained in explicit forms. Four parameter estimation methods are used to estimate the unknown parameter of the distribution. Besides, simulation study is implemented to compare the efficiencies of these parameter estimation methods. More importantly, owing to the proposed distribution, we provide an alternative regression model for the bounded response variable. The proposed regression model is compared with the beta and unit-Lindley regression models based on two real data sets.
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spelling pubmed-78223432021-01-29 A new regression model for bounded response variable: An alternative to the beta and unit-Lindley regression models Altun, Emrah El-Morshedy, M. Eliwa, M. S. PLoS One Research Article A new distribution defined on (0,1) interval is introduced. Its probability density and cumulative distribution functions have simple forms. Thanks to its simple forms, the moments, incomplete moments and quantile function of the proposed distribution are derived and obtained in explicit forms. Four parameter estimation methods are used to estimate the unknown parameter of the distribution. Besides, simulation study is implemented to compare the efficiencies of these parameter estimation methods. More importantly, owing to the proposed distribution, we provide an alternative regression model for the bounded response variable. The proposed regression model is compared with the beta and unit-Lindley regression models based on two real data sets. Public Library of Science 2021-01-22 /pmc/articles/PMC7822343/ /pubmed/33481884 http://dx.doi.org/10.1371/journal.pone.0245627 Text en © 2021 Altun et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Altun, Emrah
El-Morshedy, M.
Eliwa, M. S.
A new regression model for bounded response variable: An alternative to the beta and unit-Lindley regression models
title A new regression model for bounded response variable: An alternative to the beta and unit-Lindley regression models
title_full A new regression model for bounded response variable: An alternative to the beta and unit-Lindley regression models
title_fullStr A new regression model for bounded response variable: An alternative to the beta and unit-Lindley regression models
title_full_unstemmed A new regression model for bounded response variable: An alternative to the beta and unit-Lindley regression models
title_short A new regression model for bounded response variable: An alternative to the beta and unit-Lindley regression models
title_sort new regression model for bounded response variable: an alternative to the beta and unit-lindley regression models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7822343/
https://www.ncbi.nlm.nih.gov/pubmed/33481884
http://dx.doi.org/10.1371/journal.pone.0245627
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