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Sensitivity to Serial Dependency of Input Processes: A Robust Approach

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  • Henry Lam

    (Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109)

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 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.

Suggested Citation

  • Henry Lam, 2018. "Sensitivity to Serial Dependency of Input Processes: A Robust Approach," Management Science, INFORMS, vol. 64(3), pages 1311-1327, March.
    • Handle: RePEc:inm:ormnsc:v:64:y:2018:i:3:p:1311-1327
    DOI: 10.1287/mnsc.2016.2667
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    Citations

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    Cited by:

    1. Aleksandrina Goeva & Henry Lam & Huajie Qian & Bo Zhang, 2019. "Optimization-Based Calibration of Simulation Input Models," Operations Research, INFORMS, vol. 67(5), pages 1362-1382, September.
    2. Soumyadip Ghosh & Henry Lam, 2019. "Robust Analysis in Stochastic Simulation: Computation and Performance Guarantees," Operations Research, INFORMS, vol. 67(1), pages 232-249, January.
    3. Zhaolin Hu & L. Jeff Hong, 2022. "Robust Simulation with Likelihood-Ratio Constrained Input Uncertainty," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 2350-2367, July.
    4. Daniel Bartl & Johannes Wiesel, 2022. "Sensitivity of multiperiod optimization problems in adapted Wasserstein distance," Papers 2208.05656, arXiv.org, revised Jun 2023.
    5. Henry Lam, 2019. "Recovering Best Statistical Guarantees via the Empirical Divergence-Based Distributionally Robust Optimization," Operations Research, INFORMS, vol. 67(4), pages 1090-1105, July.

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