Model selection in utility-maximizing binary prediction

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) constructed alleviate the estimation. We establish non-asymptotic upper bounds on difference between maximal expected generalized UMPR. Simulation results show UMPR with an appropriate data-dependent penalty achieves larger than common estimators in classification if conditional probability outcome misspecified.