Estimating Optimal Decision Rules in the Presence of Model Parameter Uncertainty
提出一种自助法来估计风险最小的决策规则，适用于参数不确定的决策问题，在投资组合选择中比传统方法风险更低。
This article proposes a bootstrap procedure for estimating the risk minimizing decision-rule from within a parameterized family of rules. The procedure is conceptually simple and applicable to a broad class of decision problems involving parameter uncertainty. Moreover, when applied to Markowitz's (1952) model of portfolio selection--a leading example in which parameter uncertainty arises--it is shown to be capable of generating decision rules with lower risk than estimators derived from conventional methods. Theoretical results establishing the asymptotic optimality of the procedure are also presented. Copyright The Author, 2012. Published by Oxford University Press. All rights reserved., Oxford University Press.