The P2 algorithm for dynamic calculation of quantiles and histograms without storing observations
Communications of the ACM
Control Variates for Probability and Quantile Estimation
Management Science
Simulation-based estimation of quantiles
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Probabilistic Error Bounds for Simulation Quantile Estimators
Management Science
Variance-based sampling for cycle time: throughput confidence intervals
WSC '04 Proceedings of the 36th conference on Winter simulation
Estimation of percentiles of cycle time in manufacturing simulation
WSC '05 Proceedings of the 37th conference on Winter simulation
Cycle-time quantile estimation in manufacturing systems employing dispatching rules
WSC '05 Proceedings of the 37th conference on Winter simulation
Indirect cycle-time quantile estimation for non-FIFO dispatching policies
Proceedings of the 38th conference on Winter simulation
Using quantiles in ranking and selection procedures
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Do mean-based ranking and selection procedures consider systems' risk?
Winter Simulation Conference
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This paper introduces a new technique for estimating cycle time quantiles from discrete event simulation models run at a single traffic intensity. The Cornish-Fisher expansion is used as a vehicle for this approximation, and it is shown that for an M/M/1 system and a full factory simulation model, the technique provides accurate results with low variability for the most commonly estimated quantiles without requiring unreasonable sample sizes. Additionally, the technique provides the advantages of being easy to implement and providing multiple cycle time quantiles from a single set of simulation runs.