Non-parametric ordinal regression under a monotonicity constraint

Compared to the nominal scale, ordinal scale for a categorical outcome variable has the property of making a monotonicity assumption for the covariate effects meaningful. This assumption is encoded in the commonly used proportional odds model, but there it is combined with other parametric assumptions such as linearity and additivity. Herein, we consider models where monotonicity is used as the only modeling assumption when modeling the effects of covariates on the cumulative probabilities of ordered outcome categories. We are not aware of other non-parametric multivariable monotonic ordinal models for inference purposes. We generalize our previously proposed Bayesian monotonic multivariable regression model to ordinal outcomes, and propose an estimation procedure based on reversible jump Markov chain Monte Carlo. The model is based on a marked point process construction, which allows it to approximate arbitrary monotonic regression function shapes, and has a built-in covariate selection property. We study the performance of the proposed approach through extensive simulation studies, and demonstrate its practical application in two real data examples.