  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.
