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 ρs.

Bayes factors, Data augmentation, Latent normal, Semi-parametrics, Two-sample
dx.doi.org/10.1080/02664763.2019.1709053
Journal of Applied Statistics
Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

van Doorn, J, Ly, A, Marsman, M, & Wagenmakers, E.-J. (2020). Bayesian rank-based hypothesis testing for the rank sum test, the signed rank test, and Spearman's ρ. Journal of Applied Statistics. doi:10.1080/02664763.2019.1709053