Hierachical Resampling for Bagging in Multi-Study Prediction with Applications to Human Neurochemical Sensing

Abstract excerpt

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 f...

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Identifiers and source

Literature Corpus work

1ee62680-fcbc-5001-8ad7-3362eb795fa8

DOI

10.1101/856385