The Log-Odd Normal Generalized Family of Distributions with Application

Detalhes bibliográficos
Autor(a) principal: ZUBAIR,MUHAMMAD
Data de Publicação: 2019
Outros Autores: POGÁNY,TIBOR K., CORDEIRO,GAUSS M., TAHIR,MUHAMMAD H.
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=S0001-37652019000300203
Resumo: Abstract: The normal distribution has a central place in distribution theory and statistics. We propose the log-odd normal generalized (LONG) family of distributions based on log-odds and obtain some of its mathematical properties including a useful linear representation for the new family. We investigate, as a special model, the log-odd normal power-Cauchy (LONPC) distribution. Some structural properties of LONPC distribution are obtained including quantile function, ordinary and incomplete moments, generating function and some asymptotics. We estimate the model parameters using the maximum likelihood method. The usefulness of the proposed family is proved empirically by means of a real air pollution data set.
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spelling The Log-Odd Normal Generalized Family of Distributions with ApplicationGeneralized classmaximum likelihood estimationnormal distributionpower-Cauchy distributionShannon entropyAbstract: The normal distribution has a central place in distribution theory and statistics. We propose the log-odd normal generalized (LONG) family of distributions based on log-odds and obtain some of its mathematical properties including a useful linear representation for the new family. We investigate, as a special model, the log-odd normal power-Cauchy (LONPC) distribution. Some structural properties of LONPC distribution are obtained including quantile function, ordinary and incomplete moments, generating function and some asymptotics. We estimate the model parameters using the maximum likelihood method. The usefulness of the proposed family is proved empirically by means of a real air pollution data set.Academia Brasileira de Ciências2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000300203Anais da Academia Brasileira de Ciências v.91 n.2 2019reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765201920180207info:eu-repo/semantics/openAccessZUBAIR,MUHAMMADPOGÁNY,TIBOR K.CORDEIRO,GAUSS M.TAHIR,MUHAMMAD H.eng2019-06-27T00:00:00Zoai:scielo:S0001-37652019000300203Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2019-06-27T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false
dc.title.none.fl_str_mv The Log-Odd Normal Generalized Family of Distributions with Application
title The Log-Odd Normal Generalized Family of Distributions with Application
spellingShingle The Log-Odd Normal Generalized Family of Distributions with Application
ZUBAIR,MUHAMMAD
Generalized class
maximum likelihood estimation
normal distribution
power-Cauchy distribution
Shannon entropy
title_short The Log-Odd Normal Generalized Family of Distributions with Application
title_full The Log-Odd Normal Generalized Family of Distributions with Application
title_fullStr The Log-Odd Normal Generalized Family of Distributions with Application
title_full_unstemmed The Log-Odd Normal Generalized Family of Distributions with Application
title_sort The Log-Odd Normal Generalized Family of Distributions with Application
author ZUBAIR,MUHAMMAD
author_facet ZUBAIR,MUHAMMAD
POGÁNY,TIBOR K.
CORDEIRO,GAUSS M.
TAHIR,MUHAMMAD H.
author_role author
author2 POGÁNY,TIBOR K.
CORDEIRO,GAUSS M.
TAHIR,MUHAMMAD H.
author2_role author
author
author
dc.contributor.author.fl_str_mv ZUBAIR,MUHAMMAD
POGÁNY,TIBOR K.
CORDEIRO,GAUSS M.
TAHIR,MUHAMMAD H.
dc.subject.por.fl_str_mv Generalized class
maximum likelihood estimation
normal distribution
power-Cauchy distribution
Shannon entropy
topic Generalized class
maximum likelihood estimation
normal distribution
power-Cauchy distribution
Shannon entropy
description Abstract: The normal distribution has a central place in distribution theory and statistics. We propose the log-odd normal generalized (LONG) family of distributions based on log-odds and obtain some of its mathematical properties including a useful linear representation for the new family. We investigate, as a special model, the log-odd normal power-Cauchy (LONPC) distribution. Some structural properties of LONPC distribution are obtained including quantile function, ordinary and incomplete moments, generating function and some asymptotics. We estimate the model parameters using the maximum likelihood method. The usefulness of the proposed family is proved empirically by means of a real air pollution data set.
publishDate 2019
dc.date.none.fl_str_mv 2019-01-01
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=S0001-37652019000300203
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000300203
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0001-3765201920180207
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.91 n.2 2019
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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