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- W2765651750 abstract "We study a class of quadratic stochastic programs where the distribution of random variables has unknown parameters. A traditional approach is to estimate the parameters using a maximum likelihood estimator (MLE) and to use this as input in the optimization problem. For the unconstrained case, we show that an estimator that shrinks the MLE towards an arbitrary vector yields a uniformly better risk than the MLE. In contrast, when there are constraints, we show that the MLE is admissible." @default.
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- W2765651750 date "2017-11-01" @default.
- W2765651750 modified "2023-10-03" @default.
- W2765651750 title "From estimation to optimization via shrinkage" @default.
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- W2765651750 doi "https://doi.org/10.1016/j.orl.2017.10.005" @default.
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