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A semiparametric Bayesian approach to joint mean and variance models

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  • Xu, Dengke
  • Zhang, Zhongzhan

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.

Suggested Citation

  • Xu, Dengke & Zhang, Zhongzhan, 2013. "A semiparametric Bayesian approach to joint mean and variance models," Statistics & Probability Letters, Elsevier, vol. 83(7), pages 1624-1631.
    • Handle: RePEc:eee:stapro:v:83:y:2013:i:7:p:1624-1631
    DOI: 10.1016/j.spl.2013.02.023
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    Cited by:

    1. Dengke Xu & Zhongzhan Zhang & Liucang Wu, 2014. "Bayesian analysis of joint mean and covariance models for longitudinal data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(11), pages 2504-2514, November.
    2. Luz Marina Rondon & Heleno Bolfarine, 2016. "Bayesian analysis of generalized elliptical semi-parametric models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(8), pages 1508-1524, June.

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