A New Unbiased Stochastic Derivative Estimator for Discontinuous Sample Performances with Structural Parameters
Yijie Peng, Michael C. Fu, Jian-Qiang Hu, Bernd Heidergott
Operations Research · 2018 · Volume 66 · Issue 2
Abstract
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 .
Keywords
simulation, stochastic derivative estimation, discontinuous sample performance, likelihood ratio, perturbation analysis, weak derivative
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