A Bayesian approach for nonlinear regression models with continuous errors

被引:9
作者
de la Cruz-Mesia, R
Marshall, G
机构
[1] Pontificia Univ Catolica Chile, Fac Med, Dept Salud Publ, Santiago 22, Chile
[2] Pontificia Univ Catolica Chile, Fac Matemat, Dept Estatist, Santiago 22, Chile
关键词
continuous autoregressive process; Gibbs sampler; metropolis-Hastings algorithm within Gibbs sampler; nonlinear models;
D O I
10.1081/STA-120022248
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
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.
引用
收藏
页码:1631 / 1646
页数:16
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