Residuals and Diagnostics for Ordinal Regression Models: A Surrogate Approach.

ABSTRACT: Ordinal outcomes are common in scientific research and everyday practice, and we often rely on regression models to make inference. A long-standing problem with such regression analyses is the lack of effective diagnostic tools for validating model assumptions. The difficulty arises from the fact that an ordinal variable has discrete values that are labeled with, but not, numerical values. The values merely represent ordered categories. In this paper, we propose a surrogate approach to defining residuals for an ordinal outcome Y. The idea is to define a continuous variable S as a "surrogate" of Y and then obtain residuals based on S. For the general class of cumulative link regression models, we study the residual's theoretical and graphical properties. We show

SUBMITTER: Liu D 

PROVIDER: S-EPMC6133273 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature