Optimal Importance Sampling Parameter Search for Lévy Processes via Stochastic Approximation
Optimal Importance Sampling Parameter Search for Lévy Processes via Stochastic Approximation
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DOI：
10.1137/070680564
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发表时间：
2008-10
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影响因子：
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通讯作者：
Ray Kawai
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文献类型：
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作者：
Ray Kawai
本文提出了在Monte Carlo重要抽样方差缩减中，通过指数倾斜寻找Levy过程最优测度变化的随机逼近方法。根据潜在的Levy测度的结构，选择随机逼近的约束或无约束算法。对于这两种情况，证明了几乎处处收敛到唯一的稳定点。数值例子说明了我们的方法的有效性。
The author proposes stochastic approximation methods of finding the optimal measure change by the exponential tilting for Levy processes in Monte Carlo importance sampling variance reduction. In accordance with the structure of the underlying Levy measure, either a constrained or unconstrained algorithm of the stochastic approximation is chosen. For both cases, the almost sure convergence to a unique stationary point is proved. Numerical examples are presented to illustrate the effectiveness of our method.