Building Accurate Emulators for Stochastic Simulations via Quantile Kriging Academic Article

abstract

• 2014 American Statistical Association and the American Society for Quality. Computer simulation has increasingly become popular for analysis of systems that cannot be feasibly changed because of costs or scale. This work proposes a method to construct an emulator for stochastic simulations by performing a designed experiment on the simulator and developing an emulative distribution. Existing emulators have focused on estimation of the mean of the simulation output, but this work presents an emulator for the distribution of the output. This construction provides both an explicit distribution and a fast sampling scheme. Beyond the emulator description, this work demonstrates the emulators efficiency, that is, its convergence rate is the asymptotically optimal among all possible emulators using the same sample size (under certain conditions). An example of its practical use is demonstrated using a stochastic simulation of fracture mechanics. Supplementary materials for this article are available online.

authors

• Tuo, Rui

published proceedings

• TECHNOMETRICS

author list (cited authors)

• Plumlee, M., & Tuo, R.

citation count

• 31

complete list of authors

• Plumlee, Matthew||Tuo, Rui

publication date

• October 2014

publisher

• Taylor & Francis  Publisher

published in

• Technometrics  Journal

keywords

• Computer Experiments
• Gaussian Process
• Metric Entropy
• Quantile Regression
• Reproducing Kernel Hilbert Spaces
• Simulation Experiments

Digital Object Identifier (DOI)

• 10.1080/00401706.2013.860919

start page

• 466

end page

• 473

volume

• 56

issue

• 4

URL

• http://dx.doi.org/10.1080/00401706.2013.860919