CONVERGENCE-RATES OF FINITE-DIFFERENCE SENSITIVITY ESTIMATES FOR STOCHASTIC-SYSTEMS
成果类型：
Article
署名作者：
ZAZANIS, MA; SURI, R
署名单位：
University of Wisconsin System; University of Wisconsin Madison
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.41.4.694
发表日期：
1993
页码：
694-703
关键词：
摘要：
A mean square error analysis of finite-difference sensitivity estimators for stochastic systems is presented and an expression for the optimal size of the increment is derived. The asymptotic behavior of the optimal increments, and the behavior of the corresponding optimal finite-difference (FD) estimators are investigated for finite-horizon experiments. Steady-state estimation is also considered for regenerative systems and in this context a convergence analysis of ratio estimators is presented. The use of variance reduction techniques for these FD estimates, such as common random numbers in simulation experiments, is not considered here, In the case here, direct gradient estimation techniques (such as perturbation analysis and likelihood ratio methods) whenever applicable, are shown to converge asymptotically faster than the optimal FD estimators.