Sensitivity to Serial Dependency of Input Processes: A Robust Approach
提出一种非参数稳健方法，通过计算性能指标在最坏依赖情况下的偏差，评估序列依赖对性能分析的影响，适用于随机模型与仿真领域。
Procedures in assessing the impact of serial dependency on performance analysis are usually built on parametrically specified models. In this paper, we propose a robust, nonparametric approach to carry out this assessment, by computing the worst-case deviation of the performance measure due to arbitrary dependence. The approach is based on optimizations, posited on the model space, that have constraints specifying the level of dependency measured by a nonparametric distance to some nominal independent and identically distributed input model. We study approximation methods for these optimizations via simulation and analysis of variance. Numerical experiments demonstrate how the proposed approach can discover the hidden impacts of dependency beyond those revealed by conventional parametric modeling and correlation studies. The online appendix is available at https://doi.org/10.1287/mnsc.2016.2667 . This paper was accepted by Assaf Zeevi, stochastic models and simulation.