Bayesian analysis for semiparametric mixed-effects double regression models

Year 2016, Volume: 45 Issue: 1, 279 - 296, 01.02.2016

Dengke Xu Zhongzhan Zhang Liucang Wu

Abstract

In recent years, based on jointly modeling the mean and variance,
double regression models are widely used in practice. In order to assess the effects of continuous covariates or of time scales in a flexible
way, a class of semiparametric mixed-effects double regression models(SMMEDRMs) is considered, in which we model the variance of the
mixed effects directly as a function of the explanatory variables. In
this paper, we propose a fully Bayesian inference for SMMEDRMs on
the basis of B-spline estimates of nonparametric components. A computational efficient MCMC method which combines the Gibbs sampler
and Metropolis-Hastings algorithm is implemented to simultaneously
obtain the Bayesian estimates of unknown parameters and the smoothing function, as well as their standard deviation estimates. Finally,
some simulation studies and a real example are used to illustrate the
proposed methodology. 

Keywords

Bayesian analysis, Semiparametric mixed-effects double regression models, Gibbs sampler, Metropolis-Hastings algorithm, B-spline

References

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