MELO aggregates base predictors and their multi-scale EWLS adaptations using MLpol to achieve oracle inequalities against best fixed and time-varying predictors in non-stationary settings.
Statistical Science , year =
2 Pith papers cite this work. Polarity classification is still indexing.
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Combining stabilized weights and generalized raking yields more efficient regression estimators for two-phase sampling designs that can be implemented in standard software packages.
citing papers explorer
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Hedging Memory Horizons for Non-Stationary Prediction via Online Aggregation
MELO aggregates base predictors and their multi-scale EWLS adaptations using MLpol to achieve oracle inequalities against best fixed and time-varying predictors in non-stationary settings.
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Generalized raking and stabilized weights for regression modeling in two-phase samples
Combining stabilized weights and generalized raking yields more efficient regression estimators for two-phase sampling designs that can be implemented in standard software packages.