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Posterior property of Student-t linear regression model using objective priors

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  • Wang, Min
  • Yang, Mingan

Abstract

We derive objective priors for linear model with independent Student-t errors with unknown degrees of freedom v. It is shown that most of them preclude the existence of a proper posterior distribution unless we are willing to truncate the possible values of va priori.

Suggested Citation

  • Wang, Min & Yang, Mingan, 2016. "Posterior property of Student-t linear regression model using objective priors," Statistics & Probability Letters, Elsevier, vol. 113(C), pages 23-29.
    • Handle: RePEc:eee:stapro:v:113:y:2016:i:c:p:23-29
    DOI: 10.1016/j.spl.2016.02.003
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    References listed on IDEAS

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    1. Zellner, Arnold & Ando, Tomohiro, 2010. "A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model," Journal of Econometrics, Elsevier, vol. 159(1), pages 33-45, November.
    2. Abanto-Valle, Carlos A. & Dey, Dipak K., 2014. "State space mixed models for binary responses with scale mixture of normal distributions links," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 274-287.
    3. Thaís C. O. Fonseca & Marco A. R. Ferreira & Helio S. Migon, 2008. "Objective Bayesian analysis for the Student-t regression model," Biometrika, Biometrika Trust, vol. 95(2), pages 325-333.
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