A Bayesian approach for nonlinear regression models with continuous errors

Rolando De La Cruz-Mesía, Guillermo Marshall

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

9 Citas (Scopus)

Resumen

In this paper we develop a Bayesian analysis for the nonlinear regression model with errors that follow a continuous autoregressive process. In this way, unequally spaced observations do not present a problem in the analysis. We employ the Gibbs sampler, (see Gelfand, A., Smith, A. (1990). Sampling based approaches to calculating marginal densities. J. Amer. Statist. Assoc. 85:398-409.), as the foundation for making Bayesian inferences. We illustrate these Bayesian inferences with an analysis of a real data-set. Using these same data, we contrast the Bayesian approach with a generalized least squares technique.

Idioma originalInglés
Páginas (desde-hasta)1631-1646
Número de páginas16
PublicaciónCommunications in Statistics - Theory and Methods
Volumen32
N.º8
DOI
EstadoPublicada - ago. 2003

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