Bayesian Analysis of Multivariate Threshold Autoregressive Models with Missing Data

In some fields, we are forced to work with missing data in multivariate time series, unfortunately the analysis in this context cannot be done as in the case of complete data. Bayesian analysis of multivariate thresholds autoregressive models(MTAR) with exogenous inputs and missing data is carried o...

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Autores:
Calderón Villanueva, Sergio Alejandro
Tipo de recurso:
Doctoral thesis
Fecha de publicación:
2014
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/52159
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/52159
http://bdigital.unal.edu.co/46427/
Palabra clave:
51 Matemáticas / Mathematics
Bayesian Analysis
Bayesian variable selection
Monte Carlo Markov Chain
Missing data
Multivariate threshold autoregressive model
Análisis Bayesiano
Selección Bayesiana de variables
Cadenas de Markov Monte Carlo
Datos Faltantes
Modelos multivariados autoregressivos de umbrales
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
id UNACIONAL2_d4cfea2b00c60f6175c53ee052cc0f1a
oai_identifier_str oai:repositorio.unal.edu.co:unal/52159
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network_name_str Universidad Nacional de Colombia
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spelling Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Nieto Sánchez, Fabio HumbertoCalderón Villanueva, Sergio Alejandro81edca0c-f970-46a5-9d45-23c56251bf6a3002019-06-29T13:40:12Z2019-06-29T13:40:12Z2014-10https://repositorio.unal.edu.co/handle/unal/52159http://bdigital.unal.edu.co/46427/In some fields, we are forced to work with missing data in multivariate time series, unfortunately the analysis in this context cannot be done as in the case of complete data. Bayesian analysis of multivariate thresholds autoregressive models(MTAR) with exogenous inputs and missing data is carried out. MCMC methods are used to obtain samples from the marginal posterior distributions, including threshold values and missing data. In order to identify autoregressive orders, we adapt the Bayesian variable selection method to the MTAR models. The number of regimes is estimated using marginal likelihood and product space strategies. The forecasting of the output vector is implemented finding its predictive distributions. Simulation experiments and real data examples are presented.Resumen. En algunos campos, nos vemos forzados a trabajar con datos faltantes en series de tiempo multivaridas, desafortunadamente el análisis en este contexto no puede ser hecho como en caso completo. El análisis de modelos multivaridos autoregresivos de umbrales(MTAR) con entradas exógenas y datos faltantes es llevado a cabo vía el enfoque Bayesiano. Los métodos MCMC son usados para obtener muestras de las distribuciones marginales aposteriori, incluyendo los valores de los umbrales y los datos faltantes. Con el objetivo de identificar los órdenes autoregresivos, el método Bayesiano de selección de variables es adaptado para modelos MTAR. El número de regímenes es estimado usando la versimilitud marginal y las estrategias de espacio producto. El pronóstico para el vector de salida es implementado encontrando sus densidades predictivas. Experimentos de simulación y ejemplos con datos reales son presentados.Doctoradoapplication/pdfspaUniversidad Nacional de Colombia Sede Bogotá Facultad de Ciencias Departamento de EstadísticaDepartamento de EstadísticaCalderón Villanueva, Sergio Alejandro (2014) Bayesian Analysis of Multivariate Threshold Autoregressive Models with Missing Data. Doctorado thesis, Universidad Nacional de Colombia.51 Matemáticas / MathematicsBayesian AnalysisBayesian variable selectionMonte Carlo Markov ChainMissing dataMultivariate threshold autoregressive modelAnálisis BayesianoSelección Bayesiana de variablesCadenas de Markov Monte CarloDatos FaltantesModelos multivariados autoregressivos de umbralesBayesian Analysis of Multivariate Threshold Autoregressive Models with Missing DataTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDORIGINAL1832255.2014.pdfapplication/pdf2765786https://repositorio.unal.edu.co/bitstream/unal/52159/1/1832255.2014.pdf0c0de7b4317a4bfce6047b3a0a4c486cMD51THUMBNAIL1832255.2014.pdf.jpg1832255.2014.pdf.jpgGenerated Thumbnailimage/jpeg4029https://repositorio.unal.edu.co/bitstream/unal/52159/2/1832255.2014.pdf.jpg78b3d33e7a8ed1e71b332de9c957176cMD52unal/52159oai:repositorio.unal.edu.co:unal/521592024-02-29 23:09:06.118Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co
dc.title.spa.fl_str_mv Bayesian Analysis of Multivariate Threshold Autoregressive Models with Missing Data
title Bayesian Analysis of Multivariate Threshold Autoregressive Models with Missing Data
spellingShingle Bayesian Analysis of Multivariate Threshold Autoregressive Models with Missing Data
51 Matemáticas / Mathematics
Bayesian Analysis
Bayesian variable selection
Monte Carlo Markov Chain
Missing data
Multivariate threshold autoregressive model
Análisis Bayesiano
Selección Bayesiana de variables
Cadenas de Markov Monte Carlo
Datos Faltantes
Modelos multivariados autoregressivos de umbrales
title_short Bayesian Analysis of Multivariate Threshold Autoregressive Models with Missing Data
title_full Bayesian Analysis of Multivariate Threshold Autoregressive Models with Missing Data
title_fullStr Bayesian Analysis of Multivariate Threshold Autoregressive Models with Missing Data
title_full_unstemmed Bayesian Analysis of Multivariate Threshold Autoregressive Models with Missing Data
title_sort Bayesian Analysis of Multivariate Threshold Autoregressive Models with Missing Data
dc.creator.fl_str_mv Calderón Villanueva, Sergio Alejandro
dc.contributor.author.spa.fl_str_mv Calderón Villanueva, Sergio Alejandro
dc.contributor.spa.fl_str_mv Nieto Sánchez, Fabio Humberto
dc.subject.ddc.spa.fl_str_mv 51 Matemáticas / Mathematics
topic 51 Matemáticas / Mathematics
Bayesian Analysis
Bayesian variable selection
Monte Carlo Markov Chain
Missing data
Multivariate threshold autoregressive model
Análisis Bayesiano
Selección Bayesiana de variables
Cadenas de Markov Monte Carlo
Datos Faltantes
Modelos multivariados autoregressivos de umbrales
dc.subject.proposal.spa.fl_str_mv Bayesian Analysis
Bayesian variable selection
Monte Carlo Markov Chain
Missing data
Multivariate threshold autoregressive model
Análisis Bayesiano
Selección Bayesiana de variables
Cadenas de Markov Monte Carlo
Datos Faltantes
Modelos multivariados autoregressivos de umbrales
description In some fields, we are forced to work with missing data in multivariate time series, unfortunately the analysis in this context cannot be done as in the case of complete data. Bayesian analysis of multivariate thresholds autoregressive models(MTAR) with exogenous inputs and missing data is carried out. MCMC methods are used to obtain samples from the marginal posterior distributions, including threshold values and missing data. In order to identify autoregressive orders, we adapt the Bayesian variable selection method to the MTAR models. The number of regimes is estimated using marginal likelihood and product space strategies. The forecasting of the output vector is implemented finding its predictive distributions. Simulation experiments and real data examples are presented.
publishDate 2014
dc.date.issued.spa.fl_str_mv 2014-10
dc.date.accessioned.spa.fl_str_mv 2019-06-29T13:40:12Z
dc.date.available.spa.fl_str_mv 2019-06-29T13:40:12Z
dc.type.spa.fl_str_mv Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TD
format http://purl.org/coar/resource_type/c_db06
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/52159
dc.identifier.eprints.spa.fl_str_mv http://bdigital.unal.edu.co/46427/
url https://repositorio.unal.edu.co/handle/unal/52159
http://bdigital.unal.edu.co/46427/
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.ispartof.spa.fl_str_mv Universidad Nacional de Colombia Sede Bogotá Facultad de Ciencias Departamento de Estadística
Departamento de Estadística
dc.relation.references.spa.fl_str_mv Calderón Villanueva, Sergio Alejandro (2014) Bayesian Analysis of Multivariate Threshold Autoregressive Models with Missing Data. Doctorado thesis, Universidad Nacional de Colombia.
dc.rights.spa.fl_str_mv Derechos reservados - Universidad Nacional de Colombia
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
Derechos reservados - Universidad Nacional de Colombia
http://creativecommons.org/licenses/by-nc/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
institution Universidad Nacional de Colombia
bitstream.url.fl_str_mv https://repositorio.unal.edu.co/bitstream/unal/52159/1/1832255.2014.pdf
https://repositorio.unal.edu.co/bitstream/unal/52159/2/1832255.2014.pdf.jpg
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repository.name.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv repositorio_nal@unal.edu.co
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