Bayesian Inference for Kendall's Rank Correlation Coefficient. Issue 4 (2nd October 2018) - van Doorn, Johnny; Ly, Alexander; Marsman, Maarten; Wagenmakers, Eric-Jan
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.