New Perspectives on Statistical Decisions Under Ambiguity
这篇综述梳理了统计决策理论在模糊性下的最新进展，聚焦极小极大模型如何连接公理决策理论、估计与处理选择，并扩展到部分识别问题，适合统计与计量学者快速把握前沿。
This review summarizes and connects recent work on the foundations and applications of statistical decision theory. Minimax models of decisions making under ambiguity are identified as a thread running through several literatures. In axiomatic decision theory, these models motivated a large literature on modeling ambiguity aversion. Some findings of this literature are reported in a way that should be directly accessible to statisticians and econometricians. In statistical decision theory, the models inform a rich theory of estimation and treatment choice, which was recently extended to account for partial identification and thereby ambiguity that does not vanish with sample size. This literature is illustrated by discussing global, finite-sample admissible, and minimax decision rules for a number of stylized decision problems with point and partial identification.