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
来源：
PROCEEDINGS OF THE 2006 WINTER SIMULATION CONFERENCE, VOLS 1-5 | 2006年
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.
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页码：783 / +
页数：4
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