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Objective Bayesian inference for the intraclass correlation coefficient in linear models

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  • Zhang, Duo
  • Wang, Min

Abstract

We outline objective Bayesian testing procedure for the intraclass correlation coefficient in linear models. For it, we derive the Bayes factors based on the divergence-based priors, which have unidimensional integral expressions and can thus be easily approximated numerically.

Suggested Citation

  • Zhang, Duo & Wang, Min, 2018. "Objective Bayesian inference for the intraclass correlation coefficient in linear models," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 292-296.
    • Handle: RePEc:eee:stapro:v:137:y:2018:i:c:p:292-296
    DOI: 10.1016/j.spl.2018.02.004
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    References listed on IDEAS

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    1. Frees,Edward W., 2004. "Longitudinal and Panel Data," Cambridge Books, Cambridge University Press, number 9780521828284, September.
    2. M. J. Bayarri & G. García‐Donato, 2008. "Generalization of Jeffreys divergence‐based priors for Bayesian hypothesis testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 981-1003, November.
    3. Li, Yong & Yu, Jun, 2012. "Bayesian hypothesis testing in latent variable models," Journal of Econometrics, Elsevier, vol. 166(2), pages 237-246.
    4. Yongku Kim & Woo Dong Lee & Sang Gil Kang & Dal Ho Kim, 2017. "Objective Bayesian hypothesis testing in regression models with first-order autoregressive residuals," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(12), pages 5872-5887, June.
    5. Frees,Edward W., 2004. "Longitudinal and Panel Data," Cambridge Books, Cambridge University Press, number 9780521535380, September.
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    Cited by:

    1. Lei He & Rong-Xian Yue, 2021. "D-optimal designs for hierarchical linear models with intraclass covariance structure," Statistical Papers, Springer, vol. 62(3), pages 1349-1361, June.

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