2021/01/27 by Jon A. Steingrimsson, Steingrimsson, Jon A., Constantine Gatsonis +4 · 4 citations
Mathematics · #Applications (stat.AP) #Computer science #Context (archaeology) #Covariate #Data mining #Econometrics #FOS: Computer and information sciences #Geography #Identifiability #Machine learning #Mathematics #Mean squared error #Model selection #Population #Population model #Sample (material) #Selection (genetic algorithm) #Simple random sample #Statistical Methods and Bayesian Inference #Statistics #stat.AP
paper · pdf · doi:10.48550/arxiv.2101.11182
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2021/01/27 · arxiv created 2021/04/14 · arxiv updated 2021/04/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We consider methods for transporting a prediction model and assessing its performance for use in a new target population, when outcome and covariate information for model development is available from a simple random sample from the source population, but only covariate information is available on a simple random sample from the target population. We discuss how to tailor the prediction model for use in the target population, how to assess model performance in the target population (e.g., by estimating the target population mean squared error), and how to perform model and tuning parameter selection in the context of the target population. We provide identifiability results for the target population mean squared error of a potentially misspecified prediction model under a sampling design where the source study and the target population samples are obtained separately. We also introduce the concept of prediction error modifiers that can be used to reason about the need for tailoring measures of model performance to the target population and provide an illustration of the methods using simulated data.