Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1152964 | Statistics & Probability Letters | 2013 | 8 Pages |
Abstract
We propose a fully Bayesian inference for semiparametric joint mean and variance models on the basis of B-spline approximations of nonparametric components. An efficient MCMC method which combines Gibbs sampler and Metropolis–Hastings algorithm is suggested for the inference, and the methodology is illustrated through a simulation study and a real example.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Dengke Xu, Zhongzhan Zhang,