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A New Discrete Analog of the Continuous Lindley Distribution, with Reliability Applications

In this paper, we propose and study a new probability mass function by creating a natural discrete analog to the continuous Lindley distribution as a mixture of geometric and negative binomial distributions. The new distribution has many interesting properties that make it superior to many other dis...

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Detalles Bibliográficos
Autores principales: Al-Babtain, Abdulhakim A., Ahmed, Abdul Hadi N., Afify, Ahmed Z.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517138/
https://www.ncbi.nlm.nih.gov/pubmed/33286375
http://dx.doi.org/10.3390/e22060603
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author Al-Babtain, Abdulhakim A.
Ahmed, Abdul Hadi N.
Afify, Ahmed Z.
author_facet Al-Babtain, Abdulhakim A.
Ahmed, Abdul Hadi N.
Afify, Ahmed Z.
author_sort Al-Babtain, Abdulhakim A.
collection PubMed
description In this paper, we propose and study a new probability mass function by creating a natural discrete analog to the continuous Lindley distribution as a mixture of geometric and negative binomial distributions. The new distribution has many interesting properties that make it superior to many other discrete distributions, particularly in analyzing over-dispersed count data. Several statistical properties of the introduced distribution have been established including moments and moment generating function, residual moments, characterization, entropy, estimation of the parameter by the maximum likelihood method. A bias reduction method is applied to the derived estimator; its existence and uniqueness are discussed. Applications of the goodness of fit of the proposed distribution have been examined and compared with other discrete distributions using three real data sets from biological sciences.
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spelling pubmed-75171382020-11-09 A New Discrete Analog of the Continuous Lindley Distribution, with Reliability Applications Al-Babtain, Abdulhakim A. Ahmed, Abdul Hadi N. Afify, Ahmed Z. Entropy (Basel) Article In this paper, we propose and study a new probability mass function by creating a natural discrete analog to the continuous Lindley distribution as a mixture of geometric and negative binomial distributions. The new distribution has many interesting properties that make it superior to many other discrete distributions, particularly in analyzing over-dispersed count data. Several statistical properties of the introduced distribution have been established including moments and moment generating function, residual moments, characterization, entropy, estimation of the parameter by the maximum likelihood method. A bias reduction method is applied to the derived estimator; its existence and uniqueness are discussed. Applications of the goodness of fit of the proposed distribution have been examined and compared with other discrete distributions using three real data sets from biological sciences. MDPI 2020-05-28 /pmc/articles/PMC7517138/ /pubmed/33286375 http://dx.doi.org/10.3390/e22060603 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Al-Babtain, Abdulhakim A.
Ahmed, Abdul Hadi N.
Afify, Ahmed Z.
A New Discrete Analog of the Continuous Lindley Distribution, with Reliability Applications
title A New Discrete Analog of the Continuous Lindley Distribution, with Reliability Applications
title_full A New Discrete Analog of the Continuous Lindley Distribution, with Reliability Applications
title_fullStr A New Discrete Analog of the Continuous Lindley Distribution, with Reliability Applications
title_full_unstemmed A New Discrete Analog of the Continuous Lindley Distribution, with Reliability Applications
title_short A New Discrete Analog of the Continuous Lindley Distribution, with Reliability Applications
title_sort new discrete analog of the continuous lindley distribution, with reliability applications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517138/
https://www.ncbi.nlm.nih.gov/pubmed/33286375
http://dx.doi.org/10.3390/e22060603
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