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Bayesian Inference for Kendall's Rank Correlation Coefficient

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posted on 2016-12-15, 20:29 authored by Johnny van Doorn, Alexander Ly, Maarten Marsman, Eric-Jan Wagenmakers

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 (Johnson, 2005). 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.

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