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Density Estimators of the Cumulative Reward Up to a Hitting Time to a Rarely Visited Set of a Regenerative System

Published: 02 March 2023 Publication History

Abstract

For a regenerative process, we propose various estimators of the density function of the cumulative reward up to hitting a rarely visited set of states. The approaches exploit existing weak-convergence results for the hitting-time distribution, and we apply simulation (often with previously developed importance samplers for estimating the mean) to estimate parameters of the limiting distribution. We also combine these ideas with kernel methods. Numerical results from simulation experiments show the effectiveness of the estimators.

References

[1]
Asmussen, S., and P. Glynn. 2007. Stochastic Simulation: Algorithms and Analysis. New York: Springer.
[2]
Bagai, I., and B. L. S. Prakasa Rao. 1995. "Kernel Type Density Estimates for Positive Valued Random Variables". Sankhya 57(1):56--67.
[3]
Billingsley, P. 1995. Probability and Measure. 3rd ed. New York: John Wiley and Sons.
[4]
Dong, H., and M. K. Nakayama. 2020. "A Tutorial on Quantile Estimation via Monte Carlo". In Monte Carlo and Quasi-Monte Carlo Methods: MCQMC 2018, edited by P. L'Ecuyer and B. Tuffin: Springer Proceedings in Mathematics and Statistics.
[5]
Glynn, P. W., M. K. Nakayama, and B. Tuffin. 2017. "On the Estimation of the Mean Time to Failure by Simulation". In Proceedings of the 2017 Winter Simulation Conference, edited by W. K. V. Chan, A.D'Ambrogio, G. Zacharewicz, N. Mustafee, G. Wainer, and E. Page, 1844--1855. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.
[6]
Glynn, P. W., M. K. Nakayama, and B. Tuffin. 2018. "Using Simulation to Calibrate Exponential Approximations to Tail-Distribution Measures of Hitting Times to Rarely Visited Sets". In Proceedings of the 2018 Winter Simulation Conference, edited by M. Rabe, A. A. Juan, N. Mustafee, A. Skoogh, S. Jain, and B. Johansson, 1802--1813. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.
[7]
Goyal, A., P. Shahabuddin, P. Heidelberger, V. Nicola, and P. W. Glynn. 1992. "A Unified Framework for Simulating Markovian Models of Highly Dependable Systems". IEEE Transactions on Computers C-41(1):36--51.
[8]
Hong, L. J., Z. Hu, and G. Liu. 2014. "Monte Carlo Methods for Value-at-Risk and Conditional Value-at-Risk: A Review". ACM Transactions on Modeling and Computer Simulataion 24(4):22:1--22:37.
[9]
Kalashnikov, V. 1994. Topics on Regenerative Processes. Boca Raton: CRC Press.
[10]
Kalashnikov, V. 1997. Geometric Sums: Bounds for Rare Events with Applications. Dordrecht, The Netherlands: Kluwer Academic Publishers.
[11]
Nakayama, M. K. 2011. "Asymptotic Properties of Kernel Density Estimators When Applying Importance Sampling". In Proceedings of the 2011 Winter Simulation Conference, edited by S. Jain, R. Creasey, J. Himmelspach, K. White, and M. Fu, 556--568. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.
[12]
Nakayama, M. K., and B. Tuffin. 2019. "Efficient Estimation of the Mean Hitting Time to a Set of a Regenerative System". In Proceedings of the 2019 Winter Simulation Conference, edited by N. Mustafee, K.-H. Bae, S. Lazarova-Molnar, M. Rabe, C. Szabo, P. Haas, and Y.-J. Son, 416--427. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.
[13]
Ross, S. M. 1995. Stochastic Processes. Second ed. New York: Wiley.
[14]
Rubino, G., and B. Tuffin. (Eds.) 2009. Rare Event Simulation Using Monte Carlo Methods. Chichester, UK: John Wiley.
[15]
Sadowsky, J. S. 1991. "Large Deviations Theory and Efficient Simulation of Excessive Backlogs in a GI/GI/m Queue". IEEE Transactions on Automatic Control 36:1383--1394.
[16]
Shahabuddin, P. 1994. "Importance Sampling for Highly Reliable Markovian Systems". Management Science 40(3):333--352.
[17]
Silverman, B. W. 1986. Density Estimation for Statistics and Data Analysis. London: Chapman & Hall.
[18]
Winkler, R. L., G. M. Roodman, and R. R. Britney. 1972. "The Determination of Partial Moments". Management Science 19(3):290--296.

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cover image ACM Conferences
WSC '22: Proceedings of the Winter Simulation Conference
December 2022
3536 pages

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  • IIE: Institute of Industrial Engineers
  • INFORMS-SIM: Institute for Operations Research and the Management Sciences: Simulation Society
  • SCS: Society for Computer Simulation

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IEEE Press

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Published: 02 March 2023

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WSC '22
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WSC '22: Winter Simulation Conference
December 11 - 14, 2022
Singapore, Singapore

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