A New Unbiased Stochastic Derivative Estimator for Discontinuous Sample Performances with Structural Parameters

作者：Peng, Yijie^*; Fu, Michael C.; Hu, Jian-Qiang; Heidergott, Bernd

来源：Operations Research, 2018, 66(2): 487-499.

DOI：10.1287/opre.2017.1674

• simulation
• stochastic derivative estimation
• discontinuous sample performance
• likelihood ratio
• perturbation analysis
• weak derivative

In this paper, we propose a new unbiased stochastic derivative estimator in a framework that can handle discontinuous sample performances with structural parameters. This work extends the three most popular unbiased stochastic derivative estimators: (1) infinitesimal perturbation analysis (IPA), (2) the likelihood ratio (LR) method, and (3) the weak derivative method, to a setting where they did not previously apply. Examples in probability constraints, control charts, and financial derivatives demonstrate the broad applicability of the proposed framework. The new estimator preserves the single-run efficiency of the classic IPA-LR estimators in applications, which is substantiated by numerical experiments.

• 出版日期2018-4
• 单位复旦大学; 北京大学