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Lomax exponential distribution with an application to real-life data

In this paper, a new modification of the Lomax distribution is considered named as Lomax exponential distribution (LE). The proposed distribution is quite flexible in modeling the lifetime data with both decreasing and increasing shapes (non-monotonic). We derive the explicit expressions for the inc...

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Detalles Bibliográficos
Autores principales: Ijaz, Muhammad, Asim, Syed Muhammad, Alamgir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6905582/
https://www.ncbi.nlm.nih.gov/pubmed/31826022
http://dx.doi.org/10.1371/journal.pone.0225827
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author Ijaz, Muhammad
Asim, Syed Muhammad
Alamgir,
author_facet Ijaz, Muhammad
Asim, Syed Muhammad
Alamgir,
author_sort Ijaz, Muhammad
collection PubMed
description In this paper, a new modification of the Lomax distribution is considered named as Lomax exponential distribution (LE). The proposed distribution is quite flexible in modeling the lifetime data with both decreasing and increasing shapes (non-monotonic). We derive the explicit expressions for the incomplete moments, quantile function, the density function for the order statistics etc. The Renyi entropy for the proposed distribution is also obtained. Moreover, the paper discusses the estimates of the parameters by the usual maximum likelihood estimation method along with determining the information matrix. In addition, the potentiality of the proposed distribution is illustrated using two real data sets. To judge the performance of the model, the goodness of fit measures, AIC, CAIC, BIC, and HQIC are used. Form the results it is concluded that the proposed model performs better than the Lomax distribution, Weibull Lomax distribution, and exponential Lomax distribution.
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spelling pubmed-69055822019-12-27 Lomax exponential distribution with an application to real-life data Ijaz, Muhammad Asim, Syed Muhammad Alamgir, PLoS One Research Article In this paper, a new modification of the Lomax distribution is considered named as Lomax exponential distribution (LE). The proposed distribution is quite flexible in modeling the lifetime data with both decreasing and increasing shapes (non-monotonic). We derive the explicit expressions for the incomplete moments, quantile function, the density function for the order statistics etc. The Renyi entropy for the proposed distribution is also obtained. Moreover, the paper discusses the estimates of the parameters by the usual maximum likelihood estimation method along with determining the information matrix. In addition, the potentiality of the proposed distribution is illustrated using two real data sets. To judge the performance of the model, the goodness of fit measures, AIC, CAIC, BIC, and HQIC are used. Form the results it is concluded that the proposed model performs better than the Lomax distribution, Weibull Lomax distribution, and exponential Lomax distribution. Public Library of Science 2019-12-11 /pmc/articles/PMC6905582/ /pubmed/31826022 http://dx.doi.org/10.1371/journal.pone.0225827 Text en © 2019 Ijaz 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
Ijaz, Muhammad
Asim, Syed Muhammad
Alamgir,
Lomax exponential distribution with an application to real-life data
title Lomax exponential distribution with an application to real-life data
title_full Lomax exponential distribution with an application to real-life data
title_fullStr Lomax exponential distribution with an application to real-life data
title_full_unstemmed Lomax exponential distribution with an application to real-life data
title_short Lomax exponential distribution with an application to real-life data
title_sort lomax exponential distribution with an application to real-life data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6905582/
https://www.ncbi.nlm.nih.gov/pubmed/31826022
http://dx.doi.org/10.1371/journal.pone.0225827
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