PUMA uses model averaging to jointly handle uncertainties from model misspecification, tuning, and ML choice, delivering asymptotic in-sample and out-of-sample prediction optimality plus estimation consistency.
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Proposes Mallows-type weights for parameter-transfer learning that are asymptotically optimal for target prediction risk and selectively weight informative sources without requiring correct source models.
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Prediction-Powered Linear Regression: A Balance Between Interpretation and Prediction
PUMA uses model averaging to jointly handle uncertainties from model misspecification, tuning, and ML choice, delivering asymptotic in-sample and out-of-sample prediction optimality plus estimation consistency.
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Generalized optimal parameter-transfer learning through Mallows-type model averaging
Proposes Mallows-type weights for parameter-transfer learning that are asymptotically optimal for target prediction risk and selectively weight informative sources without requiring correct source models.