IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v156y2004i2p390-414.html
   My bibliography  Save this article

Permuted derivative and importance-sampling estimators for regenerative simulations

Author

Listed:
  • Calvin, James M.
  • Nakayama, Marvin K.

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ejores:v:156:y:2004:i:2:p:390-414
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(03)00070-5
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. James Calvin, 1994. "Return-State Independent Quantities in Regenerative Simulation," Operations Research, INFORMS, vol. 42(3), pages 531-542, June.
    2. James M. Calvin & Marvin K. Nakayama, 2000. "Central Limit Theorems for Permuted Regenerative Estimators," Operations Research, INFORMS, vol. 48(5), pages 776-787, October.
    3. 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)

    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. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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.
    6. Soumyadip Ghosh & Henry Lam, 2019. "Robust Analysis in Stochastic Simulation: Computation and Performance Guarantees," Operations Research, INFORMS, vol. 67(1), pages 232-249, January.
    7. 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.
    8. 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.
    9. L. Jeff Hong, 2009. "Estimating Quantile Sensitivities," Operations Research, INFORMS, vol. 57(1), pages 118-130, February.
    10. Jacobson, Sheldon H., 1997. "The effect of initial transient on the steady-state simulation harmonic analysis gradient estimators," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 43(2), pages 209-221.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. James M. Calvin & Marvin K. Nakayama, 2006. "Permuted Standardized Time Series for Steady-State Simulations," Mathematics of Operations Research, INFORMS, vol. 31(2), pages 351-368, May.
    16. Mark J. Cathcart & Steven Morrison & Alexander J. McNeil, 2011. "Calculating Variable Annuity Liability 'Greeks' Using Monte Carlo Simulation," Papers 1110.4516, arXiv.org.
    17. 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.
    18. 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.
    19. 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.
    20. 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.

    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:eee:ejores:v:156:y:2004:i:2:p:390-414. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.