IDEAS home Printed from https://ideas.repec.org/p/tiu/tiutis/958c9b9a-544f-48f3-a3d1-c2cf8b0a8533.html
   My bibliography  Save this paper

Optimization and Sensitivity Analysis of Computer Simulation Models by the Score Function Method

Author

Listed:
  • Kleijnen, J.P.C.

    (Tilburg University, School of Economics and Management)

  • Rubinstein, R.Y.

Abstract

No abstract is available for this item.

Suggested Citation

  • Kleijnen, J.P.C. & Rubinstein, R.Y., 1996. "Optimization and Sensitivity Analysis of Computer Simulation Models by the Score Function Method," Other publications TiSEM 958c9b9a-544f-48f3-a3d1-c, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:958c9b9a-544f-48f3-a3d1-c2cf8b0a8533
    as

    Download full text from publisher

    File URL: https://pure.uvt.nl/ws/portalfiles/portal/165147/optimiza.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kleijnen, Jack P. C. & Rubinstein, Reuven Y., 1996. "Optimization and sensitivity analysis of computer simulation models by the score function method," European Journal of Operational Research, Elsevier, vol. 88(3), pages 413-427, February.
    2. Calvin, James M. & Nakayama, Marvin K., 2004. "Permuted derivative and importance-sampling estimators for regenerative simulations," European Journal of Operational Research, Elsevier, vol. 156(2), pages 390-414, July.
    3. Abbas, K. & Heidergott, B.F. & Aissani, D., 2011. "A Taylor series expansion approach to the functional approximation of finite queues," Serie Research Memoranda 0049, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    4. Laub, Patrick J. & Salomone, Robert & Botev, Zdravko I., 2019. "Monte Carlo estimation of the density of the sum of dependent random variables," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 161(C), pages 23-31.
    5. Gilles Pages & Olivier Pironneau & Guillaume Sall, 2015. "Vibrato and Automatic Differentiation for High Order Derivatives and Sensitivities of Financial Options," Working Papers hal-01234637, HAL.
    6. Michael C. Fu, 2008. "What you should know about simulation and derivatives," Naval Research Logistics (NRL), John Wiley & Sons, vol. 55(8), pages 723-736, December.
    7. Sarazin, Gabriel & Morio, Jérôme & Lagnoux, Agnès & Balesdent, Mathieu & Brevault, Loïc, 2021. "Reliability-oriented sensitivity analysis in presence of data-driven epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    8. Akiyama, Naho & Yamada, Toshihiro, 2024. "A weak approximation for Bismut’s formula: An algorithmic differentiation method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 216(C), pages 386-396.
    9. Xin Yun & L. Jeff Hong & Guangxin Jiang & Shouyang Wang, 2019. "On gamma estimation via matrix kriging," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(5), pages 393-410, August.
    10. Soumyadip Ghosh & Henry Lam, 2019. "Robust Analysis in Stochastic Simulation: Computation and Performance Guarantees," Operations Research, INFORMS, vol. 67(1), pages 232-249, January.
    11. Yongqiang Wang & Michael C. Fu & Steven I. Marcus, 2012. "A New Stochastic Derivative Estimator for Discontinuous Payoff Functions with Application to Financial Derivatives," Operations Research, INFORMS, vol. 60(2), pages 447-460, April.
    12. Mark J. Cathcart & Steven Morrison & Alexander J. McNeil, 2011. "Calculating Variable Annuity Liability 'Greeks' Using Monte Carlo Simulation," Papers 1110.4516, arXiv.org.
    13. Li, Jinghui & Mosleh, Ali & Kang, Rui, 2011. "Likelihood ratio gradient estimation for dynamic reliability applications," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1667-1679.
    14. Jingxu Xu & Zeyu Zheng, 2023. "Gradient-Based Simulation Optimization Algorithms via Multi-Resolution System Approximations," INFORMS Journal on Computing, INFORMS, vol. 35(3), pages 633-651, May.
    15. Gilles Pag`es & Olivier Pironneau & Guillaume Sall, 2016. "Vibrato and automatic differentiation for high order derivatives and sensitivities of financial options," Papers 1606.06143, arXiv.org.
    16. Detemple, Jerome & Rindisbacher, Marcel, 2007. "Monte Carlo methods for derivatives of options with discontinuous payoffs," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3393-3417, April.
    17. Sridhar Bashyam & Michael C. Fu, 1994. "Application of perturbation analysis to a class of periodic review (s, S) inventory systems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 41(1), pages 47-80, February.
    18. Felisa J. Vazquez-Abad & Bernd Heidergott, 2003. "Gradient Estimation for a Class of Systems with Bulk Services: A Problem in Public Transportation," Tinbergen Institute Discussion Papers 03-057/4, Tinbergen Institute.
    19. Marvin K. Nakayama & Perwez Shahabuddin, 1998. "Likelihood Ratio Derivative Estimation for Finite-Time Performance Measures in Generalized Semi-Markov Processes," Management Science, INFORMS, vol. 44(10), pages 1426-1441, October.
    20. L. Jeff Hong, 2009. "Estimating Quantile Sensitivities," Operations Research, INFORMS, vol. 57(1), pages 118-130, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tiu:tiutis:958c9b9a-544f-48f3-a3d1-c2cf8b0a8533. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Richard Broekman (email available below). General contact details of provider: https://www.tilburguniversity.edu/about/schools/economics-and-management/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.