Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
Bayesian analysis of semiparametric reproductive dispersion mixed-effects models
Computational Statistics & Data Analysis
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric tests for panel count data with unequal observation processes
Computational Statistics & Data Analysis
Hi-index | 0.03 |
A generalized partial linear mixed model (GPLMM) is a natural extension of generalized linear mixed models (GLMMs) and partial linear models (PLMs). Almost all existing methods for analyzing GPLMMs are developed on the basis of the assumption that random effects are distributed as a fully parametric distribution such as normal distribution. In this paper, we extend the GPLMMs by specifying a Dirichlet process prior for a general distribution of random effects, and propose a semiparametric Bayesian approach by simultaneously utilizing an approximation truncation Dirichlet process prior of the random effects and a P-spline approximation of the smoothing function. By combining the block Gibbs sampler and the Metropolis-Hastings algorithm, a hybrid algorithm is presented for sampling observations from the posterior distribution. A procedure for selecting the degree of the polynomial components in nonparametric approximation using Bayes factor is given via path sampling. Some goodness-of-fit statistics are proposed to evaluate the plausibility of the posited model. Several simulation studies and a real example are presented to illustrate the proposed methodologies.