Sparse kernel machine regression for ordinal outcomes.

ABSTRACT: Ordinal outcomes arise frequently in clinical studies when each subject is assigned to a category and the categories have a natural order. Classification rules for ordinal outcomes may be developed with commonly used regression models such as the full continuation ratio (CR) model (fCR), which allows the covariate effects to differ across all continuation ratios, and the CR model with a proportional odds structure (pCR), which assumes the covariate effects to be constant across all continuation ratios. For settings where the covariate effects differ between some continuation ratios but not all, fitting either fCR or pCR may lead to suboptimal prediction performance. In addition, these standard models do not allow for nonlinear covariate effects. In this article, we propose a sparse CR kern

SUBMITTER: Shen Y 

PROVIDER: S-EPMC4609171 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature