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Applications of generalized likelihood ratio method to distribution sensitivities and steady-state simulation

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Abstract

We provide applications of the generalized likelihood ratio (GLR) method proposed in Peng et al. (2016c) to distribution sensitivity estimation for both finite-horizon and steady-state simulation. Applications on sensitivity of distortion risk measure, gradient-based maximum likelihood estimation, and quantile sensitivity in both finite-horizon and steady-state settings are put together under a single umbrella, and addressed uniformly by the proposed estimator. Empirical comparison of the performance of different methods is presented.

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Acknowledgments

This work was supported in part by the National Science Foundation (NSF) under Grants CMMI-1362303 and CMMI-1434419, by the National Natural Science Foundation of China (NSFC) under Grants 71571048 and 71071040, by the Air Force of Scientific Research (AFOSR) under Grant FA9550-15-10050, by the Science and Technology Agency of Sichuan Province under Grant 2014GZX0002, by the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institution of Higher Learning, and by the China Postdoctoral Science Foundation under Grant 2015M571495.

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Authors and Affiliations

  1. School of Management, Fudan University, 670 Guoshun Road, Shanghai, 200433, China

    Lei Lei, Yijie Peng & Jian-Qiang Hu

  2. Department of System Engineering and Operations Research, George Mason University, Fairfax, VA, 22030, USA

    Yijie Peng

  3. Robert H. Smith School of Business and Institute for Systems Research, University of Maryland, College Park, MD, 20742, USA

    Michael C. Fu

Authors
  1. Lei Lei
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  2. Yijie Peng
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  3. Michael C. Fu
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  4. Jian-Qiang Hu
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Correspondence to Lei Lei.

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This article belongs to the Topical Collection: Special Issue on Performance Analysis and Optimization of Discrete Event Systems

Guest Editors: Christos G. Cassandras and Alessandro Giua

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Lei, L., Peng, Y., Fu, M.C. et al. Applications of generalized likelihood ratio method to distribution sensitivities and steady-state simulation. Discrete Event Dyn Syst 28, 109–125 (2018). https://doi.org/10.1007/s10626-017-0247-8

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  • DOI: https://doi.org/10.1007/s10626-017-0247-8

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