The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Simulating Stable Stochastic Systems, VI: Quantile Estimation
Journal of the ACM (JACM)
Simulation-based estimation of quantiles
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Batching methods for simulation output analysis: a stopping procedure based on phi-mixing conditions
Proceedings of the 32nd conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Mean Estimation Based on Phi-Mixing Sequences
SS '00 Proceedings of the 33rd Annual Simulation Symposium
New simulation output analysis techniques: two-phase quantile estimation
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Quantile estimation: a minimalist approach
Proceedings of the 38th conference on Winter simulation
Estimating churn in structured P2P networks
ITC20'07 Proceedings of the 20th international teletraffic conference on Managing traffic performance in converged networks
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This paper discusses implementation of a sequential procedure to construct proportional half-width confidence intervals for a simulation estimator of the steady-state quantiles and histograms of a stochastic process. Our quasi-independent (QI) procedure increases the simulation run length progressively until a certain number of essentially independent and identically distributed samples are obtained. We compute sample quantiles at certain grid points and use Lagrange interpolation to estimate the p quantile. It is known that order statistics quantile estimator is asymptotically unbiased when the output sequences satisfy certain conditions. Even though the proposed sequential procedure is a heuristic procedure, it does have strong basis. Our empirical results show that the procedure gives quantile estimates and histograms that satisfy a pre-specified precision requirement. An experimental performance evaluation demonstrates the validity of using the QI procedure to estimate the quantiles and histograms.