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

• 成果类型: Article
• 署名作者: Peng, Yijie; Fu, Michael C.; Hu, Jian-Qiang; Heidergott, Bernd
• 署名单位: Peking University; University System of Maryland; University of Maryland College Park; Fudan University; Vrije Universiteit Amsterdam
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2017.1674
• 发表日期: 2018
• 页码: 487-499
• 关键词: smoothed perturbation analysis; monte-carlo-simulation; gradient estimation; SENSITIVITIES; systems; ipa

摘要

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.