A weighted negative binomial Lindley distribution with applications to dispersed data
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Anais da Academia Brasileira de Ciências (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652018000602617 |
Resumo: | Abstract A new discrete distribution is introduced. The distribution involves the negative binomial and size biased negative binomial distributions as sub-models among others and it is a weighted version of the two parameter discrete Lindley distribution. The distribution has various interesting properties, such as bathtub shape hazard function along with increasing/decreasing hazard rate, positive skewness, symmetric behavior, and over- and under-dispersion. Moreover, it is self decomposable and infinitely divisible, which makes the proposed distribution well suited for count data modeling. Other properties are investigated, including probability generating function, ordinary moments, factorial moments, negative moments and characterization. Estimation of the model parameters is investigated by the methods of moments and maximum likelihood, and a performance of the estimators is assessed by a simulation study. The credibility of the proposed distribution over the negative binomial, Poisson and generalized Poisson distributions is discussed based on some test statistics and four real data sets. |
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A weighted negative binomial Lindley distribution with applications to dispersed datacharacterizationdiscrete distributionsEstimationVuong test statisticmixture distributionsthunderstorms dataAbstract A new discrete distribution is introduced. The distribution involves the negative binomial and size biased negative binomial distributions as sub-models among others and it is a weighted version of the two parameter discrete Lindley distribution. The distribution has various interesting properties, such as bathtub shape hazard function along with increasing/decreasing hazard rate, positive skewness, symmetric behavior, and over- and under-dispersion. Moreover, it is self decomposable and infinitely divisible, which makes the proposed distribution well suited for count data modeling. Other properties are investigated, including probability generating function, ordinary moments, factorial moments, negative moments and characterization. Estimation of the model parameters is investigated by the methods of moments and maximum likelihood, and a performance of the estimators is assessed by a simulation study. The credibility of the proposed distribution over the negative binomial, Poisson and generalized Poisson distributions is discussed based on some test statistics and four real data sets.Academia Brasileira de Ciências2018-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652018000602617Anais da Academia Brasileira de Ciências v.90 n.3 2018reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765201820170733info:eu-repo/semantics/openAccessBAKOUCH,HASSAN Seng2019-11-29T00:00:00Zoai:scielo:S0001-37652018000602617Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2019-11-29T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false |
dc.title.none.fl_str_mv |
A weighted negative binomial Lindley distribution with applications to dispersed data |
title |
A weighted negative binomial Lindley distribution with applications to dispersed data |
spellingShingle |
A weighted negative binomial Lindley distribution with applications to dispersed data BAKOUCH,HASSAN S characterization discrete distributions Estimation Vuong test statistic mixture distributions thunderstorms data |
title_short |
A weighted negative binomial Lindley distribution with applications to dispersed data |
title_full |
A weighted negative binomial Lindley distribution with applications to dispersed data |
title_fullStr |
A weighted negative binomial Lindley distribution with applications to dispersed data |
title_full_unstemmed |
A weighted negative binomial Lindley distribution with applications to dispersed data |
title_sort |
A weighted negative binomial Lindley distribution with applications to dispersed data |
author |
BAKOUCH,HASSAN S |
author_facet |
BAKOUCH,HASSAN S |
author_role |
author |
dc.contributor.author.fl_str_mv |
BAKOUCH,HASSAN S |
dc.subject.por.fl_str_mv |
characterization discrete distributions Estimation Vuong test statistic mixture distributions thunderstorms data |
topic |
characterization discrete distributions Estimation Vuong test statistic mixture distributions thunderstorms data |
description |
Abstract A new discrete distribution is introduced. The distribution involves the negative binomial and size biased negative binomial distributions as sub-models among others and it is a weighted version of the two parameter discrete Lindley distribution. The distribution has various interesting properties, such as bathtub shape hazard function along with increasing/decreasing hazard rate, positive skewness, symmetric behavior, and over- and under-dispersion. Moreover, it is self decomposable and infinitely divisible, which makes the proposed distribution well suited for count data modeling. Other properties are investigated, including probability generating function, ordinary moments, factorial moments, negative moments and characterization. Estimation of the model parameters is investigated by the methods of moments and maximum likelihood, and a performance of the estimators is assessed by a simulation study. The credibility of the proposed distribution over the negative binomial, Poisson and generalized Poisson distributions is discussed based on some test statistics and four real data sets. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-09-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652018000602617 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652018000602617 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0001-3765201820170733 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
dc.source.none.fl_str_mv |
Anais da Academia Brasileira de Ciências v.90 n.3 2018 reponame:Anais da Academia Brasileira de Ciências (Online) instname:Academia Brasileira de Ciências (ABC) instacron:ABC |
instname_str |
Academia Brasileira de Ciências (ABC) |
instacron_str |
ABC |
institution |
ABC |
reponame_str |
Anais da Academia Brasileira de Ciências (Online) |
collection |
Anais da Academia Brasileira de Ciências (Online) |
repository.name.fl_str_mv |
Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC) |
repository.mail.fl_str_mv |
||aabc@abc.org.br |
_version_ |
1754302866016698368 |