Bayesian Inference for Kendall’s Rank Correlation Coefficient

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

The American Statistician, 2018, vol. 72, issue 4, 303-308

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.

Date: 2018
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Citations: View citations in EconPapers (3)

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DOI: 10.1080/00031305.2016.1264998

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