A New Unbiased Stochastic Derivative Estimator for Discontinuous Sample Performances with Structural Parameters
提出一种新的无偏随机导数估计方法，能处理结构参数导致的不连续样本性能，扩展了三种经典估计器的适用范围，并通过概率约束、控制图和金融衍生品等例子验证其广泛适用性和单次运行效率。
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. The online appendix is available at https://doi.org/10.1287/opre.2017.1674 .