An INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersion

Detalhes bibliográficos
Autor(a) principal: Bourguignon, Marcelo
Data de Publicação: 2017
Outros Autores: Weiss, Christian H.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRN
Texto Completo: https://repositorio.ufrn.br/handle/123456789/49725
Resumo: We present a novel first-order nonnegative integer-valued autoregressive model for stationary count data processes with Bernoulli-geometric marginals based on a new type of generalized thinning operator. It can be used for modeling time series of counts with equidispersion, underdispersion and overdispersion. The main properties of the model are derived, such as probability generating function, moments, transition probabilities and zero probability. The maximum likelihood method is used for estimating the model parameters. The proposed model is fitted to time series of counts of iceberg orders and of cases of family violence illustrating its capabilities in challenging cases of overdispersed and equidispersed count data.
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spelling Bourguignon, MarceloWeiss, Christian H.2022-11-10T19:35:33Z2022-11-10T19:35:33Z2017-04BOURGUIGNON, Marcelo; WEISS, C. H. . An INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersion. Test, v. 26, p. 847-868, 2017. Disponível em: https://link.springer.com/article/10.1007%2Fs11749-017-0536-4. Acesso em: 07 dez. 2017https://repositorio.ufrn.br/handle/123456789/4972510.1007/s11749-017-0536-4SpringerINAR(1) processBernoulli distributionGeometric distributionInteger-valued time seriesBinomial thinningNegative binomial thinningAn INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleWe present a novel first-order nonnegative integer-valued autoregressive model for stationary count data processes with Bernoulli-geometric marginals based on a new type of generalized thinning operator. It can be used for modeling time series of counts with equidispersion, underdispersion and overdispersion. The main properties of the model are derived, such as probability generating function, moments, transition probabilities and zero probability. The maximum likelihood method is used for estimating the model parameters. The proposed model is fitted to time series of counts of iceberg orders and of cases of family violence illustrating its capabilities in challenging cases of overdispersed and equidispersed count data.info:eu-repo/semantics/openAccessengreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNORIGINALAnINAR(1)process_2017.pdfAnINAR(1)process_2017.pdfapplication/pdf734390https://repositorio.ufrn.br/bitstream/123456789/49725/1/AnINAR%281%29process_2017.pdf83655b831b33c13ed3532c97b859a44cMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.ufrn.br/bitstream/123456789/49725/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52123456789/497252022-11-10 16:57:52.171oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2022-11-10T19:57:52Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false
dc.title.pt_BR.fl_str_mv An INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersion
title An INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersion
spellingShingle An INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersion
Bourguignon, Marcelo
INAR(1) process
Bernoulli distribution
Geometric distribution
Integer-valued time series
Binomial thinning
Negative binomial thinning
title_short An INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersion
title_full An INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersion
title_fullStr An INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersion
title_full_unstemmed An INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersion
title_sort An INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersion
author Bourguignon, Marcelo
author_facet Bourguignon, Marcelo
Weiss, Christian H.
author_role author
author2 Weiss, Christian H.
author2_role author
dc.contributor.author.fl_str_mv Bourguignon, Marcelo
Weiss, Christian H.
dc.subject.por.fl_str_mv INAR(1) process
Bernoulli distribution
Geometric distribution
Integer-valued time series
Binomial thinning
Negative binomial thinning
topic INAR(1) process
Bernoulli distribution
Geometric distribution
Integer-valued time series
Binomial thinning
Negative binomial thinning
description We present a novel first-order nonnegative integer-valued autoregressive model for stationary count data processes with Bernoulli-geometric marginals based on a new type of generalized thinning operator. It can be used for modeling time series of counts with equidispersion, underdispersion and overdispersion. The main properties of the model are derived, such as probability generating function, moments, transition probabilities and zero probability. The maximum likelihood method is used for estimating the model parameters. The proposed model is fitted to time series of counts of iceberg orders and of cases of family violence illustrating its capabilities in challenging cases of overdispersed and equidispersed count data.
publishDate 2017
dc.date.issued.fl_str_mv 2017-04
dc.date.accessioned.fl_str_mv 2022-11-10T19:35:33Z
dc.date.available.fl_str_mv 2022-11-10T19:35:33Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.citation.fl_str_mv BOURGUIGNON, Marcelo; WEISS, C. H. . An INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersion. Test, v. 26, p. 847-868, 2017. Disponível em: https://link.springer.com/article/10.1007%2Fs11749-017-0536-4. Acesso em: 07 dez. 2017
dc.identifier.uri.fl_str_mv https://repositorio.ufrn.br/handle/123456789/49725
dc.identifier.doi.none.fl_str_mv 10.1007/s11749-017-0536-4
identifier_str_mv BOURGUIGNON, Marcelo; WEISS, C. H. . An INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersion. Test, v. 26, p. 847-868, 2017. Disponível em: https://link.springer.com/article/10.1007%2Fs11749-017-0536-4. Acesso em: 07 dez. 2017
10.1007/s11749-017-0536-4
url https://repositorio.ufrn.br/handle/123456789/49725
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFRN
instname:Universidade Federal do Rio Grande do Norte (UFRN)
instacron:UFRN
instname_str Universidade Federal do Rio Grande do Norte (UFRN)
instacron_str UFRN
institution UFRN
reponame_str Repositório Institucional da UFRN
collection Repositório Institucional da UFRN
bitstream.url.fl_str_mv https://repositorio.ufrn.br/bitstream/123456789/49725/1/AnINAR%281%29process_2017.pdf
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