A Bayesian approach for nonlinear regression models with continuous errors

Rolando De La Cruz-Mesía, Guillermo Marshall

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish
Pages (from-to)1631-1646
Number of pages16
JournalCommunications in Statistics - Theory and Methods
Issue number8
StatePublished - Aug 2003


  • Continuous autoregressive process
  • Gibbs sampler
  • Metropolis-Hastings algorithm within Gibbs sampler
  • Nonlinear models


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