Distributional (Single) Index Models

TL;DR

The paper introduces a semi-parametric distributional index model combining parametric and non-parametric methods for distributional regression, providing calibrated probabilistic predictions that outperform existing approaches.

Contribution

It proposes a novel Distributional (Single) Index Model that integrates classical single index models with isotonic distributional regression, ensuring consistency and improved predictive performance.

Findings

Model achieves consistent estimators.

Provides calibrated probabilistic predictions.

Outperforms existing methods on ICU length of stay data.

Abstract

A Distributional (Single) Index Model (DIM) is a semi-parametric model for distributional regression, that is, estimation of conditional distributions given covariates. The method is a combination of classical single index models for the estimation of the conditional mean of a response given covariates, and isotonic distributional regression. The model for the index is parametric, whereas the conditional distributions are estimated non-parametrically under a stochastic ordering constraint. We show consistency of our estimators and apply them to a highly challenging data set on the length of stay (LoS) of patients in intensive care units. We use the model to provide skillful and calibrated probabilistic predictions for the LoS of individual patients, that outperform the available methods in the literature.