Bayesian Model Choice in Cumulative Link Ordinal Regression Models

TL;DR

This paper introduces a Bayesian approach with reversible-jump MCMC for flexible model selection between proportional and non-proportional odds in ordinal regression, effectively handling complex data with many variables.

Contribution

It develops a Bayesian framework for fitting NPO models with stochastic ordering and incorporates variable selection to choose between PO and NPO models for each predictor.

Findings

Successfully applied to dog health data in South Africa.

Demonstrated effective model selection and fitting in complex datasets.

Provided a flexible method adaptable to various monotonic link functions.

Abstract

The use of the proportional odds (PO) model for ordinal regression is ubiquitous in the literature. If the assumption of parallel lines does not hold for the data, then an alternative is to specify a non-proportional odds (NPO) model, where the regression parameters are allowed to vary depending on the level of the response. However, it is often difficult to fit these models, and challenges regarding model choice and fitting are further compounded if there are a large number of explanatory variables. We make two contributions towards tackling these issues: firstly, we develop a Bayesian method for fitting these models, that ensures the stochastic ordering conditions hold for an arbitrary finite range of the explanatory variables, allowing NPO models to be fitted to any observed data set. Secondly, we use reversible-jump Markov chain Monte Carlo to allow the model to choose between PO and NPO structures for each explanatory variable, and show how variable selection can be incorporated. These methods can be adapted for any monotonic increasing link functions. We illustrate the utility of these approaches on novel data from a longitudinal study of individual-level risk factors affecting body condition score in a dog population in Zenzele, South Africa.