Bayesian Inference for Kendall's Rank Correlation Coefficient. Issue 4 (2nd October 2018)

• Record Type: Journal Article
• Authors: van Doorn, Johnny; Ly, Alexander; Marsman, Maarten; Wagenmakers, Eric-Jan
• Is Part Of: American statistician. Volume 72:Issue 4(2018)
• Page Start: 303
• Page End: 308
• Publication Date: 2018-10-02
• Subjects: Bayes factor -- Kendall's tau -- Nonparametric inference; Statistics -- Periodicals; 001.42205
• DOI: 10.1080/00031305.2016.1264998
• Languages: English
• ISSNs: 0003-1305

Abstract

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.