Sequential procedure for simultaneous estimation of several percentiles
Transactions of the Society for Computer Simulation International
Simulating Stable Stochastic Systems, VI: Quantile Estimation
Journal of the ACM (JACM)
The Art of Computer Programming Volumes 1-3 Boxed Set
The Art of Computer Programming Volumes 1-3 Boxed Set
Simulation Modeling and Analysis
Simulation Modeling and Analysis
New simulation output analysis techniques: two-phase quantile estimation
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Empirical evaluation of data-based density estimation
Proceedings of the 38th conference on Winter simulation
An enhanced lognormal selection procedure
Discrete Event Dynamic Systems
Do mean-based ranking and selection procedures consider systems' risk?
Winter Simulation Conference
A new perspective on batched quantile estimation
Proceedings of the Winter Simulation Conference
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This paper discusses a unified approach for estimating, via a histogram, the steady-state distribution of a stochastic process observed by simulation. The quasi-independent (QI) procedure increases the simulation run length progressively until a certain number of essentially independent and identically distributed samples are obtained. It is known that order-statistics quantile estimators are asymptotically unbiased when the output sequences satisfy certain conditions. We compute sample quantiles at certain grid points and use Lagrange interpolation to estimate any p quantile. Our quantile estimators satisfy a proportional-precision requirement at the first phase, and a relative- or absolute-precision requirement at the second phase. An experimental performance evaluation demonstrates the validity of using the QI procedure to estimate quantiles and construct a histogram to estimate the steady-state distribution.