Subsampling to Enhance Efficiency in Input Uncertainty Quantification

Published Online:https://doi.org/10.1287/opre.2021.2168

References

  • Abadie A, Imbens GW (2008) On the failure of the bootstrap for matching estimators. Econometrica 76(6):1537–1557.CrossrefGoogle Scholar
  • Andrews DW, Guggenberger P (2009) Validity of subsampling and “plug-in asymptotic” inference for parameters defined by moment inequalities. Econometric Theory 25(3):669–709.CrossrefGoogle Scholar
  • Andrews DW, Guggenberger P (2010) Asymptotic size and a problem with subsampling and with the m out of n bootstrap. Econometric Theory 26(2):426–468.CrossrefGoogle Scholar
  • Asmussen S, Glynn PW (2007) Stochastic Simulation: Algorithms and Analysis, Stochastic Modeling and Applied Probability, vol. 57 (Springer Science & Business Media, New York).CrossrefGoogle Scholar
  • Barton RR (2007) A more complete characterization of uncertainty: Can it be done? Proc. 2007 INFORMS Simulation Soc. Research Workshop (INFORMS Simulation Society, Catonsville, MD), 26–60.Google Scholar
  • Barton RR (2012) Tutorial: Input uncertainty in output analysis. Laroque C, Himmelspach J, Pasupathy R, Rose O, Uhrmacher A, eds. Proc. 2012 Winter Simulation Conf. (IEEE, Piscataway, NJ), 1–12.Google Scholar
  • Barton RR, Schruben LW (1993) Uniform and bootstrap resampling of empirical distributions. Evans GW, Mollaghasemi M, Russell E, Biles W, eds. Proc. 1993 Winter Simulation Conf. (Association for Computing Machinery, New York), 503–508.Google Scholar
  • Barton RR, Schruben LW (2001) Resampling methods for input modeling. Peters BA, Smith JS, Medeiros DJ, Rohrer MW, eds. Proc. 2001 Winter Simulation Conf., vol. 1 (IEEE, Piscataway, NJ), 372–378.Google Scholar
  • Barton RR, Lam H, Song E (2018) Revisiting direct bootstrap resampling for input model uncertainty. 2018 Winter Simulation Conf. (WSC), (IEEE, Piscataway, NJ), 1635–1645.Google Scholar
  • Barton RR, Nelson BL, Xie W (2013) Quantifying input uncertainty via simulation confidence intervals. INFORMS J. Comput. 26(1):74–87.LinkGoogle Scholar
  • Barton RR, Chick SE, Cheng RC, Henderson SG, Law AM, Schmeiser BW, Leemis LM, Schruben LW, Wilson JR (2002) Panel discussion on current issues in input modeling. Yücesan E, Chen CH, Snowdon JL, Charnes JM, eds. Proc. 2002 Winter Simulation Conf. (IEEE, Piscataway, NJ), 353–369.Google Scholar
  • Bickel PJ, Sakov A (2008) On the choice of m in the m out of n bootstrap and confidence bounds for extrema. Statist. Sinica 18(3):967–985.Google Scholar
  • Bickel PJ, Götze F, van Zwet WR (1997) Resampling fewer than n observations: Gains, losses, and remedies for losses. Statist. Sinica 7(1):1–31.Google Scholar
  • Biller B, Corlu CG (2011) Accounting for parameter uncertainty in large-scale stochastic simulations with correlated inputs. Oper. Res. 59(3):661–673.LinkGoogle Scholar
  • Cheng RC, Holland W (1997) Sensitivity of computer simulation experiments to errors in input data. J. Stat. Comput. Simulation 57(1-4):219–241.CrossrefGoogle Scholar
  • Cheng RC, Holland W (1998) Two-point methods for assessing variability in simulation output. J. Stat. Comput. Simulation 60(3):183–205.CrossrefGoogle Scholar
  • Cheng RC, Holland W (2004) Calculation of confidence intervals for simulation output. ACM Trans. Model. Comput. Simulation 14(4):344–362.CrossrefGoogle Scholar
  • Chick SE (2001) Input distribution selection for simulation experiments: Accounting for input uncertainty. Oper. Res. 49(5):744–758.LinkGoogle Scholar
  • Chick SE (2006) Bayesian ideas and discrete event simulation: Why, what and how. Perrone LF, Wieland FP, Liu J, Lawson BG, Nicol DM, Fujimoto RM, eds. Proc. 2006 Winter Simulation Conf. (IEEE, Piscataway, NJ), 96–106.Google Scholar
  • Datta S, McCormick WP (1995) Bootstrap inference for a first-order autoregression with positive innovations. J. Amer. Statist. Assoc. 90(432):1289–1300.CrossrefGoogle Scholar
  • Ghosh S, Lam H (2019) Robust analysis in stochastic simulation: Computation and performance guarantees. Oper. Res. 67(1):232–249.LinkGoogle Scholar
  • Glasserman P, Xu X (2014) Robust risk measurement and model risk. Quant. Finance 14(1):29–58.CrossrefGoogle Scholar
  • Hall P, Horowitz JL, Jing BY (1995) On blocking rules for the bootstrap with dependent data. Biometrika 82(3):561–574.CrossrefGoogle Scholar
  • Hampel FR (1974) The influence curve and its role in robust estimation. J. Amer. Statist. Assoc. 69(346):383–393.CrossrefGoogle Scholar
  • Henderson SG (2003) Input modeling: Input model uncertainty: Why do we care and what should we do about it? Chick S, Sánchez PJ, Ferrin D, Morrice DJ, eds. Proc. 2003 Winter Simulation Conf. (IEEE, Piscataway, NJ), 90–100.Google Scholar
  • Hu Z, Cao J, Hong LJ (2012) Robust simulation of global warming policies using the dice model. Management Sci. 58(12):2190–2206.LinkGoogle Scholar
  • Lam H (2016a) Advanced tutorial: Input uncertainty and robust analysis in stochastic simulation. 2016 Winter Simulation Conf. (WSC) (IEEE, Piscataway, NJ), 178–192.Google Scholar
  • Lam H (2016b) Robust sensitivity analysis for stochastic systems. Math. Oper. Res. 41(4):1248–1275.LinkGoogle Scholar
  • Lam H, Qian H (2016) The empirical likelihood approach to simulation input uncertainty. 2016 Winter Simulation Conf. (WSC) (IEEE, Piscataway, NJ), 791–802.Google Scholar
  • Lam H, Qian H (2017) Optimization-based quantification of simulation input uncertainty via empirical likelihood. Preprint, submitted July 19, https://arxiv.org/abs/1707.05917.Google Scholar
  • Law AM, Kelton WD (1991) Simulation Modeling and Analysis, McGraw-Hill Series in Industrial Engineering and Management Science, vol. 2 (McGraw-Hill, New York).Google Scholar
  • Lin Y, Song E, Nelson B (2015) Single-experiment input uncertainty. J. Simulation 9(3):249–259.CrossrefGoogle Scholar
  • Nelson B (2013) Foundations and Methods of Stochastic Simulation: A First Course, International Series in Operations Research & Management Science, vol. 187 (Springer Science & Business Media, New York).CrossrefGoogle Scholar
  • Politis DN, Romano JP (1994) Large sample confidence regions based on subsamples under minimal assumptions. Ann. Statist. 22(4):2031–2050.CrossrefGoogle Scholar
  • Politis DN, Romano JP, Wolf M (1999) Subsampling, Springer Series in Statistics (Springer, New York).CrossrefGoogle Scholar
  • Sen B, Banerjee M, Woodroofe M (2010) Inconsistency of bootstrap: The Grenander estimator. Ann. Statist. 38(4):1953–1977.CrossrefGoogle Scholar
  • Serfling RJ (2009) Approximation Theorems of Mathematical Statistics, Wiley Series in Probability and Statistics, vol. 162 (John Wiley & Sons, Hoboken, NJ).Google Scholar
  • Song E, Nelson BL (2015) Quickly assessing contributions to input uncertainty. IIE Trans. 47(9):893–909.CrossrefGoogle Scholar
  • Song E, Nelson BL (2019) Input–output uncertainty comparisons for discrete optimization via simulation. Oper. Res. 67(2):562–576.AbstractGoogle Scholar
  • Song E, Nelson BL, Pegden CD (2014) Advanced tutorial: Input uncertainty quantification. Tolk A, Diallo S, Ryzhov I, Yilmaz L, Buckley S, Miller J, eds. Proc. 2014 Winter Simulation Conf. (IEEE, Piscataway, NJ), 162–176.Google Scholar
  • Sun Y, Apley DW, Staum J (2011) Efficient nested simulation for estimating the variance of a conditional expectation. Oper. Res. 59(4):998–1007.LinkGoogle Scholar
  • Sun Y, Phillips PC, Jin S (2008) Optimal bandwidth selection in heteroskedasticity—autocorrelation robust testing. Econometrica 76(1):175–194.CrossrefGoogle Scholar
  • Van der Vaart AW (2000) Asymptotic Statistics, Cambridge Studies in Statistical and Probabilistic Mathematics, vol. 3 (Cambridge University Press, Cambridge, UK).Google Scholar
  • Wieland JR, Schmeiser BW (2006) Stochastic gradient estimation using a single design point. Perrone LF, Wieland FP, Liu J, Lawson BG, Nicol DM, Fujimoto RM, eds. Proc. 2006 Winter Simulation Conf. (IEEE, Piscataway, NJ), 390–397.Google Scholar
  • Xie W, Li C, Wu Y, Zhang P (2019) A Bayesian nonparametric framework for uncertainty quantification in simulation. Preprint, submitted October 9, https://arxiv.org/abs/1910.03766.Google Scholar
  • Xie W, Nelson BL, Barton RR (2014) A Bayesian framework for quantifying uncertainty in stochastic simulation. Oper. Res. 62(6):1439–1452.LinkGoogle Scholar
  • Xie W, Nelson BL, Barton RR (2016) Multivariate input uncertainty in output analysis for stochastic simulation. ACM Trans. Model. Comput. Simulation 27(1):5.CrossrefGoogle Scholar
  • Yi Y, Xie W (2017) An efficient budget allocation approach for quantifying the impact of input uncertainty in stochastic simulation. ACM Trans. Model. Comput. Simulation 27(4):25.CrossrefGoogle Scholar
  • Zhu H, Liu T, Zhou E (2020) Risk quantification in stochastic simulation under input uncertainty. ACM Trans. Model. Comput. Simulation 30(1):1.CrossrefGoogle Scholar
  • Zouaoui F, Wilson JR (2003) Accounting for parameter uncertainty in simulation input modeling. IIE Trans. 35(9):781–792.CrossrefGoogle Scholar
  • Zouaoui F, Wilson JR (2004) Accounting for input-model and input-parameter uncertainties in simulation. IIE Trans. 36(11):1135–1151.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.