Outliers, structural shifts and heavy-tailed distributions in state space time series models

Autores
Abril, Juan Carlos
Año de publicación
2002
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work a general method is developed for handling outliers, structural shifts and heavy-tailed distributions in linear state space time series models. The basic tool we use for dealing with outliers and structural shifts is to model observation or state error densities by a mixture of densities, one component of which is a Gaussian density with a large variance. The other component can be a Gaussian density, a non-Gaussian density such as Student’s t or it can itself be a Gaussian mixture. The underlying idea is to estimate the state vector by its posterior mode using linearisation, iteration and the Kalman filter and smoother.
Fil: Abril, Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Económicas. Instituto de Investigaciones Estadísticas; Argentina
Materia
State Space
Outliers
Heavy Tails
Structural Shifts
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/105976
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/105976
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Outliers, structural shifts and heavy-tailed distributions in state space time series modelsAbril, Juan CarlosState SpaceOutliersHeavy TailsStructural Shiftshttps://purl.org/becyt/ford/5.2https://purl.org/becyt/ford/5In this work a general method is developed for handling outliers, structural shifts and heavy-tailed distributions in linear state space time series models. The basic tool we use for dealing with outliers and structural shifts is to model observation or state error densities by a mixture of densities, one component of which is a Gaussian density with a large variance. The other component can be a Gaussian density, a non-Gaussian density such as Student’s t or it can itself be a Gaussian mixture. The underlying idea is to estimate the state vector by its posterior mode using linearisation, iteration and the Kalman filter and smoother.Fil: Abril, Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Económicas. Instituto de Investigaciones Estadísticas; ArgentinaPakistan Journal of Statistics2002-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/105976Abril, Juan Carlos; Outliers, structural shifts and heavy-tailed distributions in state space time series models; Pakistan Journal of Statistics; Pakistan Journal of Statistics; 18; 1; 12-2002; 25-431012-9367CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.pakjs.com/1985-to-2016/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:49:08Zoai:ri.conicet.gov.ar:11336/105976instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:49:08.901CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Outliers, structural shifts and heavy-tailed distributions in state space time series models
title Outliers, structural shifts and heavy-tailed distributions in state space time series models
spellingShingle Outliers, structural shifts and heavy-tailed distributions in state space time series models
Abril, Juan Carlos
State Space
Outliers
Heavy Tails
Structural Shifts
title_short Outliers, structural shifts and heavy-tailed distributions in state space time series models
title_full Outliers, structural shifts and heavy-tailed distributions in state space time series models
title_fullStr Outliers, structural shifts and heavy-tailed distributions in state space time series models
title_full_unstemmed Outliers, structural shifts and heavy-tailed distributions in state space time series models
title_sort Outliers, structural shifts and heavy-tailed distributions in state space time series models
dc.creator.none.fl_str_mv Abril, Juan Carlos
author Abril, Juan Carlos
author_facet Abril, Juan Carlos
author_role author
dc.subject.none.fl_str_mv State Space
Outliers
Heavy Tails
Structural Shifts
topic State Space
Outliers
Heavy Tails
Structural Shifts
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.2
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv In this work a general method is developed for handling outliers, structural shifts and heavy-tailed distributions in linear state space time series models. The basic tool we use for dealing with outliers and structural shifts is to model observation or state error densities by a mixture of densities, one component of which is a Gaussian density with a large variance. The other component can be a Gaussian density, a non-Gaussian density such as Student’s t or it can itself be a Gaussian mixture. The underlying idea is to estimate the state vector by its posterior mode using linearisation, iteration and the Kalman filter and smoother.
Fil: Abril, Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Económicas. Instituto de Investigaciones Estadísticas; Argentina
description In this work a general method is developed for handling outliers, structural shifts and heavy-tailed distributions in linear state space time series models. The basic tool we use for dealing with outliers and structural shifts is to model observation or state error densities by a mixture of densities, one component of which is a Gaussian density with a large variance. The other component can be a Gaussian density, a non-Gaussian density such as Student’s t or it can itself be a Gaussian mixture. The underlying idea is to estimate the state vector by its posterior mode using linearisation, iteration and the Kalman filter and smoother.
publishDate 2002
dc.date.none.fl_str_mv 2002-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/105976
Abril, Juan Carlos; Outliers, structural shifts and heavy-tailed distributions in state space time series models; Pakistan Journal of Statistics; Pakistan Journal of Statistics; 18; 1; 12-2002; 25-43
1012-9367
CONICET Digital
CONICET
url http://hdl.handle.net/11336/105976
identifier_str_mv Abril, Juan Carlos; Outliers, structural shifts and heavy-tailed distributions in state space time series models; Pakistan Journal of Statistics; Pakistan Journal of Statistics; 18; 1; 12-2002; 25-43
1012-9367
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.pakjs.com/1985-to-2016/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Pakistan Journal of Statistics
publisher.none.fl_str_mv Pakistan Journal of Statistics
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613523187957760
score 13.070432

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