Bayesian Inference for Kendall’s Rank Correlation Coefficient

By J. V. van Doorn, Alexander Ly, Maarten Marsman, Eric-Jan Wagenmakers. 2018

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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.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/622e80289ab2ee74d62d3d8a2774bb61c93e939a