Model Selection in Utility-Maximizing Binary Prediction

• Tác giả: Jiun-Hua Su
• Ngôn ngữ: eng
• Ký hiệu phân loại: 688.1 Models and miniatures
• Thông tin xuất bản: 2019
• Bộ sưu tập: Metadata
• ID: 162670

Tóm tắt

 Comment: Accepted by the Journal of EconometricsThe 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.