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A Bayesian Approach for Nonlinear Regression Models with Continuous Errors.
- Source :
- Communications in Statistics: Theory & Methods; Aug2003, Vol. 32 Issue 8, p1631-1646, 16p, 2 Charts
- Publication Year :
- 2003
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03610926
- Volume :
- 32
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- Communications in Statistics: Theory & Methods
- Publication Type :
- Academic Journal
- Accession number :
- 10222945
- Full Text :
- https://doi.org/10.1081/STA-120022248