Bayesian approach for nonlinear random effects models

作     者：Dey, DK Chen, MH Chang, H 

作者机构：Univ Connecticut Dept Stat Storrs CT 06269 USA Worcester Polytech Inst Dept Math Sci Worcester MA 01609 USA Coopers & Lybrand LLP Boston MA 02110 USA 

出 版 物：《BIOMETRICS》 (Biometrics)

年 卷 期：1997年第53卷第4期

页      面：1239-1252页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 09[农学] 0714[理学-统计学（可授理学、经济学学位）] 

主　　题：Gibbs sampler longitudinal data metropolis algorithm noninformative prior nonlinear models predictive distributions pseudo-Bayes factor random effects models 

摘      要：In this paper, we propose a general model-determination strategy based on Bayesian methods for nonlinear mixed effects models. Adopting an exploratory data analysis viewpoint, we develop diagnostic tools based on conditional predictive ordinates that conveniently get tied in with Markov chain Monte Carlo fitting of models. Sampling-based methods are used to carry out these diagnostics. Two examples are presented to illustrate the effectiveness of these criteria. The first one is the famous Langmuir equation, commonly used in pharmacokinetic models, whereas the second model is used in the growth curve model for longitudinal data.