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
| Summary: | 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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