Derives necessary and sufficient conditions for vanishing regret in the censored data-driven newsvendor under a DRO ambiguity set defined by the max historical order quantity, and proposes a near-optimal adaptive algorithm with finite-sample bounds.
SIAM Journal on Scientific and Statistical Computing 4(4):706--711
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The Data-Driven Censored Newsvendor Problem
Derives necessary and sufficient conditions for vanishing regret in the censored data-driven newsvendor under a DRO ambiguity set defined by the max historical order quantity, and proposes a near-optimal adaptive algorithm with finite-sample bounds.