Bayesian Discrete Conditional Transformation Models. (arXiv:2205.08594v1 [stat.ME])

We propose a novel Bayesian model framework for discrete ordinal and count
data based on conditional transformations of the responses. The conditional
transformation function is estimated from the data in conjunction with an a
priori chosen reference distribution. For count responses, the resulting
transformation model is novel in the sense that it is a Bayesian fully
parametric yet distribution-free approach that can additionally account for
excess zeros with additive transformation function specifications. For ordinal
categoric responses, our cumulative link transformation …

arxiv bayesian transformation