PCA-based extreme sampling on the first principal component of multiple error-prone exposures yields simultaneous efficiency improvements across models in two-phase validation, demonstrated via simulations and NHANES application.
Model selection and model averaging after multiple imputation.Computational Statistics & Data Analysis.2014;71:758–770
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Two-phase validation sampling via principal components to improve efficiency in multi-model estimation from error-prone biomedical databases
PCA-based extreme sampling on the first principal component of multiple error-prone exposures yields simultaneous efficiency improvements across models in two-phase validation, demonstrated via simulations and NHANES application.