Proceedings of the 35th conference on Winter simulation: driving innovation
Estimating Quantile Sensitivities
Operations Research
Quantile Sensitivity Estimation
NET-COOP '09 Proceedings of the 3rd Euro-NF Conference on Network Control and Optimization
The impact of sampling methods on bias and variance in stochastic linear programs
Computational Optimization and Applications
Confidence intervals for quantiles when applying variance-reduction techniques
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Using sectioning to construct confidence intervals for quantiles when applying importance sampling
Proceedings of the Winter Simulation Conference
Monte Carlo estimation of value-at-risk, conditional value-at-risk and their sensitivities
Proceedings of the Winter Simulation Conference
Confidence intervals for quantiles and value-at-risk when applying importance sampling
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
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A simulation-based quantile estimator measures the level of system performance that can be delivered with a prespecified probability. To estimate selected quantiles of the response of a finite-horizon simulation, we develop procedures based on correlation induction techniques for variance reduction, with emphasis on antithetic variates and Latin hypercube sampling. These procedures achieve improved precision by controlling the simulation's random-number inputs as an integral part of the experimental design. The proposed multiple-sample quantile estimator is the average of negatively correlated quantile estimators computed from disjoint samples of the simulation response, where negative correlation is induced between corresponding responses in different samples while mutual independence of responses is maintained within each sample. The proposed single-sample quantile estimator is computed from negatively correlated simulation responses within one all-inclusive sample. The single-sample estimator based on Latin hypercube sampling is shown to be asymptotically normal and unbiased with smaller variance than the comparable directsimulation estimator based on independent replications. Similar asymptotic comparisons of the multiple-sample and directsimulation estimators focus on bias and mean square error. Monte Carlo results suggest that the proposed procedures can yield significant reductions in bias, variance, and mean square error when estimating quantiles of the completion time of a stochastic activity network.