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Optimization and sensitivity analysis of computer simulation models by the score function method

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  • Kleijnen, J.P.C.

    (Tilburg University, Center For Economic Research)

  • Rubinstein, R.Y.

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Suggested Citation

  • Kleijnen, J.P.C. & Rubinstein, R.Y., 1995. "Optimization and sensitivity analysis of computer simulation models by the score function method," Discussion Paper 1995-13, Tilburg University, Center for Economic Research.
    • Handle: RePEc:tiu:tiucen:bf6eb5a0-3fef-46ef-a6f4-991518cf878e
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    References listed on IDEAS

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    1. Rubinstein, Y.R. & Kreimer, J., 1983. "About one Monte Carlo method for solving linear equations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 25(4), pages 321-334.
    2. Martin I. Reiman & Alan Weiss, 1989. "Sensitivity Analysis for Simulations via Likelihood Ratios," Operations Research, INFORMS, vol. 37(5), pages 830-844, October.
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    Cited by:

    1. Millwater, Harry & Singh, Gulshan & Cortina, Miguel, 2012. "Development of a localized probabilistic sensitivity method to determine random variable regional importance," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 3-15.
    2. Mingbin Ben Feng & Eunhye Song, 2020. "Efficient Nested Simulation Experiment Design via the Likelihood Ratio Method," Papers 2008.13087, arXiv.org, revised May 2024.
    3. Dang, Ou & Feng, Mingbin & Hardy, Mary R., 2023. "Two-stage nested simulation of tail risk measurement: A likelihood ratio approach," Insurance: Mathematics and Economics, Elsevier, vol. 108(C), pages 1-24.
    4. Wang, Pan & Lu, Zhenzhou & Zhang, Kaichao & Xiao, Sinan & Yue, Zhufeng, 2018. "Copula-based decomposition approach for the derivative-based sensitivity of variance contributions with dependent variables," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 437-450.
    5. Tan, S.Y.G.L. & van Oortmarssen, G.J. & Piersma, N., 2000. "Estimting parameters of a microsimulation model for breast cancer screening using the score function method," Econometric Institute Research Papers EI 2000-35/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Wang, Pan & Lu, Zhenzhou & Ren, Bo & Cheng, Lei, 2013. "The derivative based variance sensitivity analysis for the distribution parameters and its computation," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 305-315.
    7. Arsham Hossein, 2007. "Monte Carlo Techniques for Parametric Finite Multidimensional Integral Equations," Monte Carlo Methods and Applications, De Gruyter, vol. 13(3), pages 173-195, August.
    8. Shih, Neng-Hui, 1999. "The sensitivity analysis of binary networks via simulation," European Journal of Operational Research, Elsevier, vol. 114(3), pages 602-609, May.

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