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Model Selection in Utility-Maximizing Binary Prediction (accepted by JOURNAL OF ECONOMETRICS)

作者 蘇俊華
摘要 The semiparametric 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 in the machine learning literature. Based on structural risk minimization, 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 some common estimators in the traditional binary classification if the conditional probability of the binary outcome is misspecified.