Jackknife estimators for reducing bias in asset allocation

被引:6
|
作者
Partani, Amit [1 ]
Morton, David P. [1 ]
Popova, Ivilina [2 ]
机构
[1] Univ Texas, Grad Program Operat Res, Austin, TX 78712 USA
[2] Seattle Univ, Albers Sch Business & Econ, Seattle, WA 98122 USA
基金
美国国家科学基金会;
关键词
D O I
10.1109/WSC.2006.323159
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We use jackknife-based estimators to reduce bias when estimating the optimal value of a stochastic program. Our discussion focuses on an asset allocation model with a power utility function. As we will describe, estimating the optimal value of such a problem plays a key role in establishing the quality of a candidate solution, and reducing bias improves our ability to do so efficiently. We develop a jackknife estimator that is adaptive in that it does not assume the order of the bias is known a priori.
引用
收藏
页码:783 / +
页数:4
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