Cross-validation for nonlinear mixed effects models.

ABSTRACT: Cross-validation is frequently used for model selection in a variety of applications. However, it is difficult to apply cross-validation to mixed effects models (including nonlinear mixed effects models or NLME models) due to the fact that cross-validation requires "out-of-sample" predictions of the outcome variable, which cannot be easily calculated when random effects are present. We describe two novel variants of cross-validation that can be applied to NLME models. One variant, where out-of-sample predictions are based on post hoc estimates of the random effects, can be used to select the overall structural model. Another variant, where cross-validation seeks to minimize the estimated random effects rather than the estimated residuals, can be used to select covariates to include in the

SUBMITTER: Colby E 

PROVIDER: S-EPMC3668859 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature