TY - JOUR
T1 - A Bayesian approach for nonlinear regression models with continuous errors
AU - De La Cruz-Mesía, Rolando
AU - Marshall, Guillermo
N1 - Funding Information:
We would like to thank the referee for comments that improved the presentation of this paper. This paper was partially funded by the Sciences and Technology Foundation of Chile; grant number: Fondecyt 1010958.
PY - 2003/8
Y1 - 2003/8
N2 - 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.
AB - 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.
KW - Continuous autoregressive process
KW - Gibbs sampler
KW - Metropolis-Hastings algorithm within Gibbs sampler
KW - Nonlinear models
UR - http://www.scopus.com/inward/record.url?scp=0041704573&partnerID=8YFLogxK
U2 - 10.1081/STA-120022248
DO - 10.1081/STA-120022248
M3 - Article
AN - SCOPUS:0041704573
SN - 0361-0926
VL - 32
SP - 1631
EP - 1646
JO - Communications in Statistics - Theory and Methods
JF - Communications in Statistics - Theory and Methods
IS - 8
ER -