Introduces the constrained multiplier criterion for misspecification-averse estimation and proves its asymptotic optimality via a local minimax theorem in a limit experiment incorporating moment constraints.
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Proposes APUB optimization framework for stochastic programming, proves asymptotic correctness and consistency of the new bound, and develops bootstrap and L-shaped solvers for two-stage linear problems with empirical tests on a product mix example.
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Misspecification-Averse Estimation
Introduces the constrained multiplier criterion for misspecification-averse estimation and proves its asymptotic optimality via a local minimax theorem in a limit experiment incorporating moment constraints.
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Minimizing Upper Confidence Bounds: A Data-Driven Framework for Stochastic Programming
Proposes APUB optimization framework for stochastic programming, proves asymptotic correctness and consistency of the new bound, and develops bootstrap and L-shaped solvers for two-stage linear problems with empirical tests on a product mix example.