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Bayesian Inference for Kendall’s Rank Correlation Coefficient

Author

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

Abstract

This article outlines a Bayesian methodology to estimate and test the Kendall rank correlation coefficient τ. The nonparametric nature of rank data implies the absence of a generative model and the lack of an explicit likelihood function. These challenges can be overcome by modeling test statistics rather than data. We also introduce a method for obtaining a default prior distribution. The combined result is an inferential methodology that yields a posterior distribution for Kendall’s τ.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:amstat:v:72:y:2018:i:4:p:303-308
    DOI: 10.1080/00031305.2016.1264998
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    Cited by:

    1. 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.
    2. Almeida, Lucas Milanez de Lima & Balanco, Paulo Antonio de Freitas, 2020. "Application of multivariate analysis as complementary instrument in studies about structural changes: An example of the multipliers in the US economy," Structural Change and Economic Dynamics, Elsevier, vol. 53(C), pages 189-207.
    3. Arran T Reader & H Henrik Ehrsson, 2019. "Weakening the subjective sensation of own hand ownership does not interfere with rapid finger movements," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-28, October.

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