期刊
OPERATIONS RESEARCH
卷 66, 期 2, 页码 487-499出版社
INFORMS
DOI: 10.1287/opre.2017.1674
关键词
simulation; stochastic derivative estimation; discontinuous sample performance; likelihood ratio; perturbation analysis; weak derivative
资金
- National Natural Science Foundation of China [71571048, 71720107003, 71690232, 71371015]
- National Science Foundation [CMMI-0856256, CMMI-1362303, CMMI-1434419]
- Air Force Office of Scientific Research [FA9550-15-10050]
- Science and Technology Agency of Sichuan Province [2014GZX0002]
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.
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