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Bayesian estimation of Kendall’s τ using a latent normal approach

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  • van Doorn, Johnny
  • Ly, Alexander
  • Marsman, Maarten
  • Wagenmakers, Eric-Jan

Abstract

The rank-based association between two variables can be modeled by introducing a latent normal level to ordinal data. We demonstrate how this approach yields Bayesian inference for Kendall’s τ, improving on a recent Bayesian solution based on its asymptotic properties.

Suggested Citation

  • van Doorn, Johnny & Ly, Alexander & Marsman, Maarten & Wagenmakers, Eric-Jan, 2019. "Bayesian estimation of Kendall’s τ using a latent normal approach," Statistics & Probability Letters, Elsevier, vol. 145(C), pages 268-272.
    • Handle: RePEc:eee:stapro:v:145:y:2019:i:c:p:268-272
    DOI: 10.1016/j.spl.2018.10.004
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    References listed on IDEAS

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    1. Johnny van Doorn & Alexander Ly & Maarten Marsman & Eric-Jan Wagenmakers, 2018. "Bayesian Inference for Kendall’s Rank Correlation Coefficient," The American Statistician, Taylor & Francis Journals, vol. 72(4), pages 303-308, October.
    2. Valen E. Johnson, 2005. "Bayes factors based on test statistics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 689-701, November.
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