IDEAS home Printed from https://ideas.repec.org/a/inm/ormoor/v26y2001i2p375-400.html
   My bibliography  Save this article

Computing Densities for Markov Chains via Simulation

Author

Listed:
  • Shane G. Henderson

    () (Department of Industrial and Operations Engineering, University of Michigan, 1205 Beal Avenue, Ann Arbor, MI 48109-2117)

  • Peter W. Glynn

    () (Management Science and Engineering, Terman Engineering Center, Stanford University, Stanford, CA 94305-4026)

Abstract

We introduce a new class of density estimators, termed look-ahead density estimators, for performance measures associated with a Markov chain. Look-ahead density estimators are given for both transient and steady-state quantities. Look-ahead density estimators converge faster (especially in multidimensional problems) and empirically give visually superior results relative to more standard estimators, such as kernel density estimators. Several numerical examples that demonstrate the potential applicability of look-ahead density estimation are given.

Suggested Citation

  • Shane G. Henderson & Peter W. Glynn, 2001. "Computing Densities for Markov Chains via Simulation," Mathematics of Operations Research, INFORMS, vol. 26(2), pages 375-400, May.
    • Handle: RePEc:inm:ormoor:v:26:y:2001:i:2:p:375-400
    DOI: 10.1287/moor.26.2.375.10562
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/moor.26.2.375.10562
    Download Restriction: no

    References listed on IDEAS

    as
    1. Athanassios N. Avramidis & James R. Wilson, 1996. "Integrated Variance Reduction Strategies for Simulation," Operations Research, INFORMS, vol. 44(2), pages 327-346, April.
    2. Andrew F. Seila, 1982. "A Batching Approach to Quantile Estimation in Regenerative Simulations," Management Science, INFORMS, vol. 28(5), pages 573-581, May.
    3. Yakowitz, Sidney, 1989. "Nonparametric density and regression estimation for Markov sequences without mixing assumptions," Journal of Multivariate Analysis, Elsevier, vol. 30(1), pages 124-136, July.
    4. P. Heidelberger & P. A. W. Lewis, 1984. "Quantile Estimation in Dependent Sequences," Operations Research, INFORMS, vol. 32(1), pages 185-209, February.
    5. Athanassios N. Avramidis & James R. Wilson, 1998. "Correlation-Induction Techniques for Estimating Quantiles in Simulation Experiments," Operations Research, INFORMS, vol. 46(4), pages 574-591, August.
    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. Azariadis, Costas & Stachurski, John, 2005. "Poverty Traps," Handbook of Economic Growth, in: Philippe Aghion & Steven Durlauf (ed.),Handbook of Economic Growth, edition 1, volume 1, chapter 5, Elsevier.
    2. John Stachurski & Vance Martin, 2008. "Computing the Distributions of Economic Models via Simulation," Econometrica, Econometric Society, vol. 76(2), pages 443-450, March.
    3. R. Anton Braun & Huiyu Li & John Stachurski, 2012. "Generalized Look-Ahead Methods for Computing Stationary Densities," Mathematics of Operations Research, INFORMS, vol. 37(3), pages 489-500, August.
    4. Christian Gourieroux & Joann Jasiak, 2016. "Filtering, Prediction and Simulation Methods for Noncausal Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(3), pages 405-430, May.
    5. R. Anton Braun & Huiyu Li & John Stachurski, 2011. "Generalized Look-Ahead Methods for Computing Stationary Densities," ANU Working Papers in Economics and Econometrics 2011-558, Australian National University, College of Business and Economics, School of Economics.
    6. Richard Anton Braun & Huiyu Li & John Stachurski, 2009. "Computing Densities: A Conditional Monte Carlo Estimator," CIRJE F-Series CIRJE-F-678, CIRJE, Faculty of Economics, University of Tokyo.
    7. Richard Anton Braun & Huiyu Li & John Stachurski, 2009. "Computing Densities and Expectations in Stochastic Recursive Economies: Generalized Look-Ahead Techniques," CIRJE F-Series CIRJE-F-620, CIRJE, Faculty of Economics, University of Tokyo.
    8. John Stachurski & Huiyu Li & Richard Anton Braun, 2009. "Computing Densities in Stochastic Recursive Economies: Generalized Look-Ahead Techniques," 2009 Meeting Papers 975, Society for Economic Dynamics.

    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. Christos Alexopoulos & David Goldsman & Anup C. Mokashi & Kai-Wen Tien & James R. Wilson, 2019. "Sequest: A Sequential Procedure for Estimating Quantiles in Steady-State Simulations," Operations Research, INFORMS, vol. 67(4), pages 1162-1183, July.
    2. E Saliby & R J Paul, 2009. "A farewell to the use of antithetic variates in Monte Carlo simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(7), pages 1026-1035, July.
    3. L. Jeff Hong, 2009. "Estimating Quantile Sensitivities," Operations Research, INFORMS, vol. 57(1), pages 118-130, February.
    4. T. Glenn Bailey & Paul A. Jensen & David P. Morton, 1999. "Response surface analysis of two‐stage stochastic linear programming with recourse," Naval Research Logistics (NRL), John Wiley & Sons, vol. 46(7), pages 753-776, October.
    5. Jong Jun Park & Geon Ho Choe, 2016. "A new variance reduction method for option pricing based on sampling the vertices of a simplex," Quantitative Finance, Taylor & Francis Journals, vol. 16(8), pages 1165-1173, August.
    6. Guangwu Liu & Liu Jeff Hong, 2009. "Kernel estimation of quantile sensitivities," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(6), pages 511-525, September.
    7. Kilic, Onur A. & Tunc, Huseyin & Tarim, S. Armagan, 2018. "Heuristic policies for the stochastic economic lot sizing problem with remanufacturing under service level constraints," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1102-1109.
    8. Mingbin Ben Feng & Eunhye Song, 2020. "Optimal Nested Simulation Experiment Design via Likelihood Ratio Method," Papers 2008.13087, arXiv.org.
    9. Jeff Linderoth & Alexander Shapiro & Stephen Wright, 2006. "The empirical behavior of sampling methods for stochastic programming," Annals of Operations Research, Springer, vol. 142(1), pages 215-241, February.
    10. Lacour, Claire, 2008. "Nonparametric estimation of the stationary density and the transition density of a Markov chain," Stochastic Processes and their Applications, Elsevier, vol. 118(2), pages 232-260, February.
    11. N-H Shih, 2005. "Estimating completion-time distribution in stochastic activity networks," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(6), pages 744-749, June.
    12. Athanassios N. Avramidis & James R. Wilson, 1998. "Correlation-Induction Techniques for Estimating Quantiles in Simulation Experiments," Operations Research, INFORMS, vol. 46(4), pages 574-591, August.
    13. L. Jeff Hong & Guangwu Liu, 2010. "Pathwise Estimation of Probability Sensitivities Through Terminating or Steady-State Simulations," Operations Research, INFORMS, vol. 58(2), pages 357-370, April.
    14. Liebscher, Eckhard, 1996. "Strong convergence of sums of [alpha]-mixing random variables with applications to density estimation," Stochastic Processes and their Applications, Elsevier, vol. 65(1), pages 69-80, December.
    15. Chen, E. Jack & Kelton, W. David, 2006. "Quantile and tolerance-interval estimation in simulation," European Journal of Operational Research, Elsevier, vol. 168(2), pages 520-540, January.
    16. Chaitra H. Nagaraja & Haikady N. Nagaraja, 2020. "Distribution‐free Approximate Methods for Constructing Confidence Intervals for Quantiles," International Statistical Review, International Statistical Institute, vol. 88(1), pages 75-100, April.
    17. Moloche, Guillermo, 2001. "Local Nonparametric Estimation of Scalar Diffusions," MPRA Paper 46154, University Library of Munich, Germany.
    18. Hatem Ben-Ameur & Pierre L'Ecuyer & Christiane Lemieux, 2004. "Combination of General Antithetic Transformations and Control Variables," Mathematics of Operations Research, INFORMS, vol. 29(4), pages 946-960, November.
    19. Hui Dong & Marvin K. Nakayama, 2017. "Quantile Estimation with Latin Hypercube Sampling," Operations Research, INFORMS, vol. 65(6), pages 1678-1695, December.
    20. Xi Chen & Kyoung-Kuk Kim, 2016. "Efficient VaR and CVaR Measurement via Stochastic Kriging," INFORMS Journal on Computing, INFORMS, vol. 28(4), pages 629-644, November.

    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:inm:ormoor:v:26:y:2001:i:2:p:375-400. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Matthew Walls). General contact details of provider: http://edirc.repec.org/data/inforea.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.