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UNA APROXIMACIÓN BAYESIANA AL PROBLEMA DE HETEROSCEDASTICIDAD EN EL MODELO LINEAL SIMPLE
Keywords:
Heteroscedasticidad, modelos de regresión, estadística bayesiana, muestreador de Gibbs (es)Heteroscedasticity, Regression models, Bayesian statistics, Gibbs sampler (en)
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1Profesor asociado. Escuela de Estadística. Universidad Nacional. Sede Medellín. E-mail: jccorrea@unalmed.edu.co
Presentamos una implementación bayesiana para ayudar a resolver un pro blema de heteroscedaticidad en el modelo de regresión simple, fácilmente extendible al caso múltiple.
Palabras Clave: Heteroscedasticidad, modelos de regresión, estadística bayesiana, muestreador de Gibbs.
We implement a bayesian solution to the heteroscedasticity problem in simple regression. This solution can be easily generalized to the multiple regression case.
Keywords: Heteroscedasticity, Regression models, Bayesian statistics, Gibbs sampler.
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Referencias
1. Gelfand, A. & Smith, A. F. M. (1990), "Sampling based approaches to calculating marginal densities", J. Amer. Stat. Assoc 85, 398- 409.
2. Geman, S. & Geman, D. (1984), "Stochastic relaxation, Gibbs distributions and the bayesian restoration of images", IEEE Trans. on Pattern Analysis and Machine Intelligence 6, 721- 741.
3. Lee, P. M. (1997), Bayesian Statistics: An Introduction, 2 edn, Arnold, London.
4. Tanner, M. A. (1996), Tools for Statistical Inference, Springer-Verlag, New York.
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Copyright (c) 2005 Revista Colombiana de Estadística
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