A competitive family to the Beta and Kumaraswamy generators: Properties, Regressions and Applications

Detalhes bibliográficos
Autor(a) principal: CORDEIRO,GAUSS M.
Data de Publicação: 2022
Outros Autores: VASCONCELOS,JULIO CEZAR S., ORTEGA,EDWIN M.M., MARINHO,PEDRO RAFAEL D.
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=S0001-37652022000300301
Resumo: Abstract We define two new flexible families of continuous distributions to fit real data by compoun-ding the Marshall–Olkin class and the power series distribution. These families are very competitive to the popular beta and Kumaraswamy generators. Their densities have linear representations of exponentiated densities. In fact, as the main properties of thirty five exponentiated distributions are well-known, we can easily obtain several properties of about three hundred fifty distributions using the references of this article and five special cases of the power series distribution. We provide a package implemented in R software that shows numerically the precision of one of the linear representations. This package is useful to calculate numerical values for some statistical measurements of the generated distributions. We estimate the parameters by maximum likelihood. We define a regression based on one of the two families. The usefulness of a generated distribution and the associated regression is proved empirically.
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spelling A competitive family to the Beta and Kumaraswamy generators: Properties, Regressions and Applicationsgenerating functionMarshall–Olkin familymaximum likelihoodmomentdistributionAbstract We define two new flexible families of continuous distributions to fit real data by compoun-ding the Marshall–Olkin class and the power series distribution. These families are very competitive to the popular beta and Kumaraswamy generators. Their densities have linear representations of exponentiated densities. In fact, as the main properties of thirty five exponentiated distributions are well-known, we can easily obtain several properties of about three hundred fifty distributions using the references of this article and five special cases of the power series distribution. We provide a package implemented in R software that shows numerically the precision of one of the linear representations. This package is useful to calculate numerical values for some statistical measurements of the generated distributions. We estimate the parameters by maximum likelihood. We define a regression based on one of the two families. The usefulness of a generated distribution and the associated regression is proved empirically.Academia Brasileira de Ciências2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000300301Anais da Academia Brasileira de Ciências v.94 n.2 2022reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765202220201972info:eu-repo/semantics/openAccessCORDEIRO,GAUSS M.VASCONCELOS,JULIO CEZAR S.ORTEGA,EDWIN M.M.MARINHO,PEDRO RAFAEL D.eng2022-07-14T00:00:00Zoai:scielo:S0001-37652022000300301Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2022-07-14T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false
dc.title.none.fl_str_mv A competitive family to the Beta and Kumaraswamy generators: Properties, Regressions and Applications
title A competitive family to the Beta and Kumaraswamy generators: Properties, Regressions and Applications
spellingShingle A competitive family to the Beta and Kumaraswamy generators: Properties, Regressions and Applications
CORDEIRO,GAUSS M.
generating function
Marshall–Olkin family
maximum likelihood
moment
distribution
title_short A competitive family to the Beta and Kumaraswamy generators: Properties, Regressions and Applications
title_full A competitive family to the Beta and Kumaraswamy generators: Properties, Regressions and Applications
title_fullStr A competitive family to the Beta and Kumaraswamy generators: Properties, Regressions and Applications
title_full_unstemmed A competitive family to the Beta and Kumaraswamy generators: Properties, Regressions and Applications
title_sort A competitive family to the Beta and Kumaraswamy generators: Properties, Regressions and Applications
author CORDEIRO,GAUSS M.
author_facet CORDEIRO,GAUSS M.
VASCONCELOS,JULIO CEZAR S.
ORTEGA,EDWIN M.M.
MARINHO,PEDRO RAFAEL D.
author_role author
author2 VASCONCELOS,JULIO CEZAR S.
ORTEGA,EDWIN M.M.
MARINHO,PEDRO RAFAEL D.
author2_role author
author
author
dc.contributor.author.fl_str_mv CORDEIRO,GAUSS M.
VASCONCELOS,JULIO CEZAR S.
ORTEGA,EDWIN M.M.
MARINHO,PEDRO RAFAEL D.
dc.subject.por.fl_str_mv generating function
Marshall–Olkin family
maximum likelihood
moment
distribution
topic generating function
Marshall–Olkin family
maximum likelihood
moment
distribution
description Abstract We define two new flexible families of continuous distributions to fit real data by compoun-ding the Marshall–Olkin class and the power series distribution. These families are very competitive to the popular beta and Kumaraswamy generators. Their densities have linear representations of exponentiated densities. In fact, as the main properties of thirty five exponentiated distributions are well-known, we can easily obtain several properties of about three hundred fifty distributions using the references of this article and five special cases of the power series distribution. We provide a package implemented in R software that shows numerically the precision of one of the linear representations. This package is useful to calculate numerical values for some statistical measurements of the generated distributions. We estimate the parameters by maximum likelihood. We define a regression based on one of the two families. The usefulness of a generated distribution and the associated regression is proved empirically.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
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=S0001-37652022000300301
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000300301
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0001-3765202220201972
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.94 n.2 2022
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
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