Linear and Non-Linear Regression Models Assuming a Stable Distribution
In this paper, we present some computational aspects for a Bayesiananalysis involving stable distributions. It is well known that, in general, there is no closed form for the probability density function of a stable distribution. However, the use of a latent or auxiliary random variable facilitates...
- Autores:
-
Achcar, Jorge A.
Lopes, Sílvia R. C.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2016
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/66526
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/66526
http://bdigital.unal.edu.co/67554/
- Palabra clave:
- 51 Matemáticas / Mathematics
31 Colecciones de estadística general / Statistics
Stable Laws
Bayesian Analysis
Mcmc Methods
OpenBUGS Software
Leyes estable
Análisis bayesiano
Métodos MCMC
Software OpenBUGS.
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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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_abf2Achcar, Jorge A.4c8699e8-9605-4982-a560-ecdeff8a0259300Lopes, Sílvia R. C.9a83bab0-50b0-4086-b39a-aa1276f3a0553002019-07-03T02:18:16Z2019-07-03T02:18:16Z2016-01-01ISSN: 2389-8976https://repositorio.unal.edu.co/handle/unal/66526http://bdigital.unal.edu.co/67554/In this paper, we present some computational aspects for a Bayesiananalysis involving stable distributions. It is well known that, in general, there is no closed form for the probability density function of a stable distribution. However, the use of a latent or auxiliary random variable facilitates obtaining any posterior distribution when related to stable distributions. To show the usefulness of the computational aspects, the methodology is applied to linear and non-linear regression models. Posterior summaries of interest are obtained using the OpenBUGS software.En este trabajo, presentamos algunos aspectos computacionales de análisis bayesiano con distribuciones estables. Es bien sabido que, en general, no hay forma cerrada para la función de densidad de probabilidad de distribuciones estables. Sin embargo, el uso de una variable aleatoria latente facilita obtener la distribución a posteriori. La metodología se aplica a regresión lineal y non lineal utilizando el software OpenBUGS.application/pdfspaUniversidad Nacional de Colombia - Sede Bogotá - Facultad de Ciencias - Departamento de Estadísticahttps://revistas.unal.edu.co/index.php/estad/article/view/55144Universidad Nacional de Colombia Revistas electrónicas UN Revista Colombiana de EstadísticaRevista Colombiana de EstadísticaAchcar, Jorge A. and Lopes, Sílvia R. C. (2016) Linear and Non-Linear Regression Models Assuming a Stable Distribution. Revista Colombiana de Estadística, 39 (1). pp. 109-128. ISSN 2389-897651 Matemáticas / Mathematics31 Colecciones de estadística general / StatisticsStable LawsBayesian AnalysisMcmc MethodsOpenBUGS SoftwareLeyes estableAnálisis bayesianoMétodos MCMCSoftware OpenBUGS.Linear and Non-Linear Regression Models Assuming a Stable DistributionArtículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTORIGINAL55144-279463-1-PB.pdfapplication/pdf635981https://repositorio.unal.edu.co/bitstream/unal/66526/1/55144-279463-1-PB.pdf1b2f6840a320f3a3dcfed7020b4001abMD51THUMBNAIL55144-279463-1-PB.pdf.jpg55144-279463-1-PB.pdf.jpgGenerated Thumbnailimage/jpeg5206https://repositorio.unal.edu.co/bitstream/unal/66526/2/55144-279463-1-PB.pdf.jpg723bb72aafa55ea99d36ae1c34e10fdeMD52unal/66526oai:repositorio.unal.edu.co:unal/665262023-05-25 23:03:06.323Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co |
| dc.title.spa.fl_str_mv |
Linear and Non-Linear Regression Models Assuming a Stable Distribution |
| title |
Linear and Non-Linear Regression Models Assuming a Stable Distribution |
| spellingShingle |
Linear and Non-Linear Regression Models Assuming a Stable Distribution 51 Matemáticas / Mathematics 31 Colecciones de estadística general / Statistics Stable Laws Bayesian Analysis Mcmc Methods OpenBUGS Software Leyes estable Análisis bayesiano Métodos MCMC Software OpenBUGS. |
| title_short |
Linear and Non-Linear Regression Models Assuming a Stable Distribution |
| title_full |
Linear and Non-Linear Regression Models Assuming a Stable Distribution |
| title_fullStr |
Linear and Non-Linear Regression Models Assuming a Stable Distribution |
| title_full_unstemmed |
Linear and Non-Linear Regression Models Assuming a Stable Distribution |
| title_sort |
Linear and Non-Linear Regression Models Assuming a Stable Distribution |
| dc.creator.fl_str_mv |
Achcar, Jorge A. Lopes, Sílvia R. C. |
| dc.contributor.author.spa.fl_str_mv |
Achcar, Jorge A. Lopes, Sílvia R. C. |
| dc.subject.ddc.spa.fl_str_mv |
51 Matemáticas / Mathematics 31 Colecciones de estadística general / Statistics |
| topic |
51 Matemáticas / Mathematics 31 Colecciones de estadística general / Statistics Stable Laws Bayesian Analysis Mcmc Methods OpenBUGS Software Leyes estable Análisis bayesiano Métodos MCMC Software OpenBUGS. |
| dc.subject.proposal.spa.fl_str_mv |
Stable Laws Bayesian Analysis Mcmc Methods OpenBUGS Software Leyes estable Análisis bayesiano Métodos MCMC Software OpenBUGS. |
| description |
In this paper, we present some computational aspects for a Bayesiananalysis involving stable distributions. It is well known that, in general, there is no closed form for the probability density function of a stable distribution. However, the use of a latent or auxiliary random variable facilitates obtaining any posterior distribution when related to stable distributions. To show the usefulness of the computational aspects, the methodology is applied to linear and non-linear regression models. Posterior summaries of interest are obtained using the OpenBUGS software. |
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2016 |
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2016-01-01 |
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2019-07-03T02:18:16Z |
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2019-07-03T02:18:16Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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ISSN: 2389-8976 |
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https://repositorio.unal.edu.co/handle/unal/66526 |
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http://bdigital.unal.edu.co/67554/ |
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ISSN: 2389-8976 |
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https://repositorio.unal.edu.co/handle/unal/66526 http://bdigital.unal.edu.co/67554/ |
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spa |
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spa |
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https://revistas.unal.edu.co/index.php/estad/article/view/55144 |
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Universidad Nacional de Colombia Revistas electrónicas UN Revista Colombiana de Estadística Revista Colombiana de Estadística |
| dc.relation.references.spa.fl_str_mv |
Achcar, Jorge A. and Lopes, Sílvia R. C. (2016) Linear and Non-Linear Regression Models Assuming a Stable Distribution. Revista Colombiana de Estadística, 39 (1). pp. 109-128. ISSN 2389-8976 |
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Derechos reservados - Universidad Nacional de Colombia |
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Atribución-NoComercial 4.0 Internacional |
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info:eu-repo/semantics/openAccess |
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