Sensitivity to Serial Dependency of Input Processes: A Robust Approach

TL;DR

This paper introduces a robust, nonparametric method to evaluate how serial dependencies in input processes affect performance measures, surpassing traditional parametric models by revealing hidden dependency impacts.

Contribution

It proposes a novel nonparametric optimization-based approach to assess serial dependency effects, independent of specific parametric assumptions.

Findings

Detects hidden impacts of dependency beyond parametric models

Uses simulation and ANOVA for approximation methods

Numerical experiments validate the approach's effectiveness

Abstract

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 i.i.d. input model. We study approximation methods for these optimizations via simulation and analysis-of-variance (ANOVA). Numerical experiments demonstrate how the proposed approach can discover the hidden impacts of dependency beyond those revealed by conventional parametric modeling and correlation studies.