Kumaraswamy log-logistic Weibull distribution: model, theory and application to lifetime and survival data

We develop the new Kumaraswamy Log-Logistic Weibull (KLLoGW) distribution by combining the Kumaraswamy and Log-logistic Weibull distributions. This new model is flexible for modelling lifetime data. Some statistical properties including quantile function, hazard rate function, moments and conditiona...

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Detalles Bibliográficos
Autores principales: Mdlongwa, P., Oluyede, B.O., Amey, A.K.A., Fagbamigbe, A.F., Makubate, B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350220/
https://www.ncbi.nlm.nih.gov/pubmed/30723825
http://dx.doi.org/10.1016/j.heliyon.2019.e01144
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author Mdlongwa, P.
Oluyede, B.O.
Amey, A.K.A.
Fagbamigbe, A.F.
Makubate, B.
author_facet Mdlongwa, P.
Oluyede, B.O.
Amey, A.K.A.
Fagbamigbe, A.F.
Makubate, B.
author_sort Mdlongwa, P.
collection PubMed
description We develop the new Kumaraswamy Log-Logistic Weibull (KLLoGW) distribution by combining the Kumaraswamy and Log-logistic Weibull distributions. This new model is flexible for modelling lifetime data. Some statistical properties including quantile function, hazard rate function, moments and conditional moments are presented. Model parameters are estimated via the method of maximum likelihood and a Monte Carlo simulation study conducted to assess the accuracy of the estimates. Finally, the model is applied to a real dataset.
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spelling pubmed-63502202019-02-05 Kumaraswamy log-logistic Weibull distribution: model, theory and application to lifetime and survival data Mdlongwa, P. Oluyede, B.O. Amey, A.K.A. Fagbamigbe, A.F. Makubate, B. Heliyon Article We develop the new Kumaraswamy Log-Logistic Weibull (KLLoGW) distribution by combining the Kumaraswamy and Log-logistic Weibull distributions. This new model is flexible for modelling lifetime data. Some statistical properties including quantile function, hazard rate function, moments and conditional moments are presented. Model parameters are estimated via the method of maximum likelihood and a Monte Carlo simulation study conducted to assess the accuracy of the estimates. Finally, the model is applied to a real dataset. Elsevier 2019-01-21 /pmc/articles/PMC6350220/ /pubmed/30723825 http://dx.doi.org/10.1016/j.heliyon.2019.e01144 Text en © 2019 The Authors. Published by Elsevier Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Mdlongwa, P.
Oluyede, B.O.
Amey, A.K.A.
Fagbamigbe, A.F.
Makubate, B.
Kumaraswamy log-logistic Weibull distribution: model, theory and application to lifetime and survival data
title Kumaraswamy log-logistic Weibull distribution: model, theory and application to lifetime and survival data
title_full Kumaraswamy log-logistic Weibull distribution: model, theory and application to lifetime and survival data
title_fullStr Kumaraswamy log-logistic Weibull distribution: model, theory and application to lifetime and survival data
title_full_unstemmed Kumaraswamy log-logistic Weibull distribution: model, theory and application to lifetime and survival data
title_short Kumaraswamy log-logistic Weibull distribution: model, theory and application to lifetime and survival data
title_sort kumaraswamy log-logistic weibull distribution: model, theory and application to lifetime and survival data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350220/
https://www.ncbi.nlm.nih.gov/pubmed/30723825
http://dx.doi.org/10.1016/j.heliyon.2019.e01144
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