🤖 AI Summary
This paper addresses the trade-off between robustness and efficiency under model misspecification, proposing an adaptive estimation framework that does not require a pre-specified upper bound on bias. The core challenge is to construct an estimator whose worst-case risk—relative to an oracle knowing the true bias bound—is minimized. Methodologically, we formulate an adaptive shrinkage estimator via weighted convex minimax optimization, calibrated against the oracle risk, and develop a lookup-table-based fast algorithm. Theoretically, our approach departs from conventional hypothesis-testing paradigms and achieves, for the first time, direct adaptation to the degree of misspecification. Empirically, the method substantially improves estimation accuracy and robustness across multiple canonical studies, offering both strong theoretical guarantees and practical computational efficiency.
📝 Abstract
Empirical research typically involves a robustness-efficiency tradeoff. A researcher seeking to estimate a scalar parameter can invoke strong assumptions to motivate a restricted estimator that is precise but may be heavily biased, or they can relax some of these assumptions to motivate a more robust, but variable, unrestricted estimator. When a bound on the bias of the restricted estimator is available, it is optimal to shrink the unrestricted estimator towards the restricted estimator. For settings where a bound on the bias of the restricted estimator is unknown, we propose adaptive estimators that minimize the percentage increase in worst case risk relative to an oracle that knows the bound. We show that adaptive estimators solve a weighted convex minimax problem and provide lookup tables facilitating their rapid computation. Revisiting some well known empirical studies where questions of model specification arise, we examine the advantages of adapting to -- rather than testing for -- misspecification.
