Linear and Non-Linear Regression Models Assuminga Stable Distribution
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JORGE A. ACHCAR [1] ; SÍLVIA R. C. LOPES [2]
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[1] Medical School-USP
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[2] Mathematics Institute-UFRGS
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- Localización: Revista Colombiana de Estadística, ISSN-e 2389-8976, ISSN 0120-1751, Vol. 39, Nº. 1, 2016, págs. 109-128
- Idioma: inglés
- DOI: 10.15446/rce.v39n1.55144
- Títulos paralelos:
- Modelos de regressión lineal y no linealsuponiendo una distribución estable
- Enlaces
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Resumen
- español
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 metodologia se aplica a regresión lineal y non lineal utilizando el software OpenBUGS.
- English
In this paper, we present some computational aspects for a Bayesian analysis 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.
- español
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