A semiparametric Bayesian approach to joint mean and variance models

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.

MoreLess

Year of publication:	2013
Authors:	Xu, Dengke ; Zhang, Zhongzhan
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 83.2013, 7, p. 1624-1631
Publisher:	Elsevier
Subject:	Bayesian analysis \| Joint mean and variance models \| Gibbs sampler \| Metropolis–Hastings algorithm \| B-spline

Online Resource

Check full text access |

More access options

Check Google Scholar

In libraries world-wide (WorldCat)

In German libraries (KVK)

subito order

I need help

More details

Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010665608