Hierarchical resampling for bagging in multistudy prediction with applications to human neurochemical sensing.

ABSTRACT: We propose the "study strap ensemble", which combines advantages of two common approaches to fitting prediction models when multiple training datasets ("studies") are available: pooling studies and fitting one model versus averaging predictions from multiple models each fit to individual studies. The study strap ensemble fits models to bootstrapped datasets, or "pseudo-studies." These are generated by resampling from multiple studies with a hierarchical resampling scheme that generalizes the randomized cluster bootstrap. The study strap is controlled by a tuning parameter that determines the proportion of observations to draw from each study. When the parameter is set to its lowest value, each pseudo-study is resampled from only a single study. When it is high, the study strap ignores the

SUBMITTER: Loewinger G 

PROVIDER: S-EPMC9586160 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature