Model selection in utility-maximizing binary prediction
Journal of Econometrics, 2021, vol. 223, issue 1, 96-124
The maximum utility estimation proposed by Elliott and Lieli (2013) can be viewed as cost-sensitive binary classification; thus, its in-sample overfitting issue is similar to that of perceptron learning. A utility-maximizing prediction rule (UMPR) is constructed to alleviate the in-sample overfitting of the maximum utility estimation. We establish non-asymptotic upper bounds on the difference between the maximal expected utility and the generalized expected utility of the UMPR. Simulation results show that the UMPR with an appropriate data-dependent penalty achieves larger generalized expected utility than common estimators in the binary classification if the conditional probability of the binary outcome is misspecified.
Keywords: Decision-based binary prediction; Maximum utility estimation; Model selection; Structural risk minimization; Perceptron learning (search for similar items in EconPapers)
JEL-codes: C14 C45 C52 C53 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:223:y:2021:i:1:p:96-124
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