Asymptotic Properties for Cumulative Probability Models for Continuous Outcomes.

ABSTRACT: Regression models for continuous outcomes frequently require a transformation of the outcome, which is often specified a priori or estimated from a parametric family. Cumulative probability models (CPMs) nonparametrically estimate the transformation by treating the continuous outcome as if it is ordered categorically. They thus represent a flexible analysis approach for continuous outcomes. However, it is difficult to establish asymptotic properties for CPMs due to the potentially unbounded range of the transformation. Here we show asymptotic properties for CPMs when applied to slightly modified data where bounds, one lower and one upper, are chosen and the outcomes outside the bounds are set as two ordinal categories. We prove the uniform consistency of the estimated regression coefficien

SUBMITTER: Li C 

PROVIDER: S-EPMC10875740 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature