An interpretable neural network-based non-proportional odds model for ordinal regression

arXiv:2303.17823v4 Announce Type: replace-cross
Abstract: This study proposes an interpretable neural network-based non-proportional odds model (N$^3$POM) for ordinal regression. N$^3$POM is different from conventional approaches to ordinal regression with non-proportional models in several ways: (1) N$^3$POM is defined for both continuous and discrete responses, whereas standard methods typically treat the ordered continuous variables as if they are discrete, (2) instead of estimating response-dependent finite-dimensional coefficients of linear models from discrete responses as is done in conventional approaches, we train a …

abstract arxiv continuous cs.lg network neural network ordinal regression responses standard stat.me stat.ml study type