Distributional (Single) Index Models

• 成果类型: Article
• 署名作者: Henzi, Alexander; Kleger, Gian-Reto; Ziegel, Johanna F.
• 署名单位: University of Bern; Kantonsspital St. Gallen
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.1938582
• 发表日期: 2023
• 页码: 489-503
• 关键词: length-of-stay; conditional distribution; quantile regression; acute physiology; Scoring rules; forecasts

摘要

A Distributional (Single) Index Model (DIM) is a semiparametric 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 nonparametrically under a stochastic ordering constraint. We show consistency of our estimators and apply them to a highly challenging dataset 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, which outperform the available methods in the literature.