Journal
JOURNAL OF APPLIED STATISTICS
Volume 47, Issue 16, Pages 2984-3006Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/02664763.2019.1709053
Keywords
Bayes factors; data augmentation; latent normal; two-sample; semi-parametrics
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Funding
- Netherlands Organization of Scientific Research (Nederlandse Organisatie voor Wetenschappelijk Onderzoek) (NWO) [016.Vici.170.083]
- NWO [451-17-017]
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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 .
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