Bayesian Analysis for Semiparametric Reproductive Dispersion Models with Nonignorably Missing Data
带有不可忽略缺失数据的半参数再生散度模型的贝叶斯分析

Semiparametric reproductive dispersion model (SRDNM) is an extension of reproductive dispersion models and semiparametric regression models, and includes generalized partial linear model and semiparametric generalized linear model as its special cases. A method is proposed to obtain Bayesian estimationand to select appropriate model based on Bayes factor for such modelwith missing data both in covariate and response. Firstly, nonparametric components are fitted by penalized-splines and a Bayesian hierarchical model is set to model smooth parameters, then latent variables are introduced and the collapsed Gibbs sampler is implemented in order to improve the mixing androbustness of MCMC. Finally, simulation and real datasets are presented to illustrate the proposed methods.