Bayesian rank-based hypothesis testing for the rank sum test, the signed rank test, and Spearman's ρ. Issue 16 (9th December 2020)

• Record Type: Journal Article
• Authors: van Doorn, J.; Ly, A.; Marsman, M.; Wagenmakers, E.-J.
• Is Part Of: Journal of applied statistics. Volume 47:Issue 16(2020)
• Page Start: 2984
• Page End: 3006
• Publication Date: 2020-12-09
• Subjects: Bayes factors -- data augmentation -- latent normal -- two-sample -- semi-parametrics; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2019.1709053
• Languages: English
• ISSNs: 0266-4763

Abstract

Abstract : Bayesian inference for rank-order problems is frustrated by the absence of an explicit likelihood function. This hurdle can be overcome by assuming a latent normal representation that is consistent with the ordinal information in the data: the observed ranks are conceptualized as an impoverished reflection of an underlying continuous scale, and inference concerns the parameters that govern the latent representation. We apply this generic data-augmentation method to obtain Bayes factors for three popular rank-based tests: the rank sum test, the signed rank test, and Spearman's ρ s .