The P2 algorithm for dynamic calculation of quantiles and histograms without storing observations
Communications of the ACM
Sequential procedure for simultaneous estimation of several percentiles
Transactions of the Society for Computer Simulation International
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
Approximations for Digital Computers
Approximations for Digital Computers
Indirect cycle-time quantile estimation for non-FIFO dispatching policies
Proceedings of the 38th conference on Winter simulation
Estimating steady-state distributions via simulation-generated histograms
Computers and Operations Research
Kernel estimation for quantile sensitivities
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
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We investigate the properties and robustness of histogram approximation to construct non-functional-form metamodels for estimating quantiles of systems with one controllable parameter, providing a general trend of quantiles. By non-functional-form metamodel, we mean the metamodel is not described by a single formula. The procedure constructs histograms by tracking sample quantiles at certain grid points. The algorithm dynamically increases the sample size so that the quantile estimates obtained via the histogram satisfy the proportional precision. The non-functional-form metamodel is constructed with a set of carefully selected histograms. Quantiles can then be estimated via the metamodel. An experimental performance evaluation demonstrates the validity of using non-functional-form metamodels to estimate quantiles.