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Spectral properties of MCMC algorithms for Bayesian linear regression with generalized hyperbolic errors

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  • Jung, Yeun Ji
  • Hobert, James P.

Abstract

We study MCMC algorithms for Bayesian analysis of a linear regression model with generalized hyperbolic errors. The Markov operators associated with the standard data augmentation algorithm and a sandwich variant of that algorithm are shown to be trace-class.

Suggested Citation

  • Jung, Yeun Ji & Hobert, James P., 2014. "Spectral properties of MCMC algorithms for Bayesian linear regression with generalized hyperbolic errors," Statistics & Probability Letters, Elsevier, vol. 95(C), pages 92-100.
    • Handle: RePEc:eee:stapro:v:95:y:2014:i:c:p:92-100
    DOI: 10.1016/j.spl.2014.07.034
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    References listed on IDEAS

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    1. Choi, Hee Min & Hobert, James P., 2013. "Analysis of MCMC algorithms for Bayesian linear regression with Laplace errors," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 32-40.
    2. Roy, Vivekananda & Hobert, James P., 2010. "On Monte Carlo methods for Bayesian multivariate regression models with heavy-tailed errors," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1190-1202, May.
    3. Fernández, Carmen & Steel, Mark F.J., 2000. "Bayesian Regression Analysis With Scale Mixtures Of Normals," Econometric Theory, Cambridge University Press, vol. 16(1), pages 80-101, February.
    4. Jones, Galin L. & Haran, Murali & Caffo, Brian S. & Neath, Ronald, 2006. "Fixed-Width Output Analysis for Markov Chain Monte Carlo," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1537-1547, December.
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    Cited by:

    1. Qin, Qian & Hobert, James P., 2018. "Trace-class Monte Carlo Markov chains for Bayesian multivariate linear regression with non-Gaussian errors," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 335-345.

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