Quantile plots of the prediction variance for response surface designs
Computational Statistics & Data Analysis
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Variance-based sampling for cycle time: throughput confidence intervals
WSC '04 Proceedings of the 36th conference on Winter simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
A discrete event simulation model simplification technique
WSC '05 Proceedings of the 37th conference on Winter simulation
Design of computer experiments: space filling and beyond
Statistics and Computing
Proceedings of the Winter Simulation Conference
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The use of simulation as a modeling and analysis tool is wide spread. Simulation is an enabling tool for experimenting virtually on a validated computer environment. Often the underlying function for the results of a computer simulation experiment has too much curvature to be adequately modeled by a low order polynomial. In such cases finding an appropriate experimental design is not easy. This research uses prediction variance over the volume of the design region to evaluate computer simulation experiments assuming the modeler is interested in fitting a second order polynomial or a Gaussian Process model to the response data. Both space-filling and optimal designs are considered.