Management Science
Operations Research
The spatial correlation function approach to response surface estimation
WSC '92 Proceedings of the 24th conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Using Ranking and Selection to "Clean Up" after Simulation Optimization
Operations Research
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)
Kriging metamodeling in constrained simulation optimization: an explorative study
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Stochastic kriging for simulation metamodeling
Proceedings of the 40th Conference on Winter Simulation
Computers and Operations Research
Better simulation metamodeling: the why, what, and how of stochastic kriging
Winter Simulation Conference
Winter Simulation Conference
Estimating expected shortfall with stochastic kriging
Winter Simulation Conference
Kriging metamodel with modified nugget-effect: The heteroscedastic variance case
Computers and Industrial Engineering
The effects of common random numbers on stochastic kriging metamodels
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Cases for the nugget in modeling computer experiments
Statistics and Computing
Global sensitivity analysis of stochastic computer models with joint metamodels
Statistics and Computing
Bayesian Kriging Analysis and Design for Stochastic Simulations
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Robust Optimization in Simulation: Taguchi and Krige Combined
INFORMS Journal on Computing
Input uncertainty in outout analysis
Proceedings of the Winter Simulation Conference
Optimization via simulation with Bayesian statistics and dynamic programming
Proceedings of the Winter Simulation Conference
Stochastic kriging for conditional value-at-risk and its sensitivities
Proceedings of the Winter Simulation Conference
Moving least squares regression for high dimensional simulation metamodeling
Proceedings of the Winter Simulation Conference
Efficient discrete optimization via simulation using stochastic kriging
Proceedings of the Winter Simulation Conference
On direct gradient enhanced simulation metamodels
Proceedings of the Winter Simulation Conference
Convex and monotonic bootstrapped kriging
Proceedings of the Winter Simulation Conference
Multiple input and multiple output simulation metamodeling using Bayesian networks
Proceedings of the Winter Simulation Conference
Relative error stochastic kriging
Proceedings of the Winter Simulation Conference
Risk estimation via weighted regression
Proceedings of the Winter Simulation Conference
Optimization via simulation using Gaussian process-based search
Proceedings of the Winter Simulation Conference
Common random numbers and stochastic kriging
Proceedings of the Winter Simulation Conference
A Bayesian metamodeling approach for stochastic simulations
Proceedings of the Winter Simulation Conference
The influence of correlation functions on stochastic kriging metamodels
Proceedings of the Winter Simulation Conference
A framework for input uncertainty analysis
Proceedings of the Winter Simulation Conference
Simulation on demand for pricing many securities
Proceedings of the Winter Simulation Conference
Proceedings of the International Conference on Computer-Aided Design
Stochastic kriging with biased sample estimates
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A benchmark of kriging-based infill criteria for noisy optimization
Structural and Multidisciplinary Optimization
Stochastic resource allocation using a predictor-based heuristic for optimization via simulation
Computers and Operations Research
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We extend the basic theory of kriging, as applied to the design and analysis of deterministic computer experiments, to the stochastic simulation setting. Our goal is to provide flexible, interpolation-based metamodels of simulation output performance measures as functions of the controllable design or decision variables, or uncontrollable environmental variables. To accomplish this, we characterize both the intrinsic uncertainty inherent in a stochastic simulation and the extrinsic uncertainty about the unknown response surface. We use tractable examples to demonstrate why it is critical to characterize both types of uncertainty, derive general results for experiment design and analysis, and present a numerical example that illustrates the stochastic kriging method.