Framework for federated learning with missing data that identifies conditions favoring complete-case estimators over inverse-probability weighting and proposes a calibrated weighting approach consistent if at least one candidate model is correct.
Journal of the American Statistical Association , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
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stat.ME 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A doubly robust, asymptotically normal estimator for regression with completely missing covariates across populations, combining importance weighting and moment imputation under a sub-population shift assumption.
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Federated Learning with Incomplete Data: When to Use Complete Cases and When to Weight
Framework for federated learning with missing data that identifies conditions favoring complete-case estimators over inverse-probability weighting and proposes a calibrated weighting approach consistent if at least one candidate model is correct.
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Augmented transfer regression learning for completely missing covariates
A doubly robust, asymptotically normal estimator for regression with completely missing covariates across populations, combining importance weighting and moment imputation under a sub-population shift assumption.