A new approach to unbiased estimation for SDE's

A new approach to unbiased estimation for SDE's
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SDE 无偏估计的新方法

DOI:
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发表时间:
2012
期刊:
Online World Conference on Soft Computing in Industrial Applications
影响因子:
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通讯作者:
P. Glynn
P. Glynn
中科院分区:
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文献类型:
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作者:
C. Rhee;P. Glynn

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在本文中,我们介绍了一种新的方法来构造无偏估计时,计算期望的路径泛函与随机微分方程(SDEs)。我们的随机化的想法是密切相关的多层次蒙特卡罗和提供了一个简单的机制,用于构建一个有限方差无偏估计与“平方根收敛速度”,只要有一个计划,产生强大的错误的顺序大于1/2的路径功能正在考虑。
In this paper, we introduce a new approach to constructing unbiased estimators when computing expectations of path functionals associated with stochastic differential equations (SDEs). Our randomization idea is closely related to multi-level Monte Carlo and provides a simple mechanism for constructing a finite variance unbiased estimator with “square root convergence rate” whenever one has available a scheme that produces strong error of order greater than 1/2 for the path functional under consideration.
DOI: 10.1287/opre.1070.0496
发表时间: 2008-05-01
影响因子: 2.7
作者:
Giles, Michael B.
通讯作者: Giles, Michael B.