Bayesian analysis of semiparametric reproductive dispersion mixed-effects models

Abstract

Semiparametric reproductive dispersion mixed-effects model (SPRDMM) is an extension of the reproductive dispersion model and the semiparametric mixed model, and it includes many commonly encountered models as its special cases. A Bayesian procedure is developed for analyzing SPRDMMs on the basis of P-spline estimates of nonparametric components. A hybrid algorithm combining the Gibbs sampler and the Metropolis-Hastings algorithm is used to simultaneously obtain the Bayesian estimates of unknown parameters, smoothing function and random effects, as well as their standard error estimates. The Bayes factor for model comparison is employed to select better approximation of the smoothing function via path sampling. Several simulation studies and a real example are used to illustrate the proposed methodologies.