A semiparametric Bayesian approach to joint mean and variance models

Dengke Xu and Zhongzhan Zhang

Statistics & Probability Letters, 2013, vol. 83, issue 7, 1624-1631

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.

Keywords: Bayesian analysis; Joint mean and variance models; Gibbs sampler; Metropolis–Hastings algorithm; B-spline (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.spl.2013.02.023

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