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A Batching Approach to Quantile Estimation in Regenerative Simulations

Author

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  • Andrew F. Seila

    (University of Georgia)

Abstract

This paper presents a new method, called the batch quantile method, for estimating quantiles in regenerative simulations. The quantile estimator is consistent and asymptotically normal, and the method can be easily implemented and does not require prior knowledge of the range of values the data will assume. Empirical studies show adequate coverage of confidence intervals when batches of 50 cycles or more are used.

Suggested Citation

  • Andrew F. Seila, 1982. "A Batching Approach to Quantile Estimation in Regenerative Simulations," Management Science, INFORMS, vol. 28(5), pages 573-581, May.
    • Handle: RePEc:inm:ormnsc:v:28:y:1982:i:5:p:573-581
    DOI: 10.1287/mnsc.28.5.573
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Songhao Wang & Szu Hui Ng & William Benjamin Haskell, 2022. "A Multilevel Simulation Optimization Approach for Quantile Functions," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 569-585, January.
    4. 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.
    5. Park, Dae S. & Kim, Yun B. & Shin, Key I. & Willemain, Thomas R., 2001. "Simulation output analysis using the threshold bootstrap," European Journal of Operational Research, Elsevier, vol. 134(1), pages 17-28, October.
    6. 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.

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