Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/94738
Title: Gaussian process single-index models as emulators for computer experiments
Authors: Gramacy, Robert B.
Lian, Heng
Issue Date: 2012
Source: Gramacy, R. B., & Lian, H. (2012). Gaussian Process Single-Index Models as Emulators for Computer Experiments. Technometrics, 54(1), 30-41.
Series/Report no.: Technometrics
Abstract: A single-index model (SIM) provides for parsimonious multidimensional nonlinear regression by combining parametric (linear) projection with univariate nonparametric (nonlinear) regression models. We show that a particular Gaussian process (GP) formulation is simple to work with and ideal as an emulator for some types of computer experiment as it can outperform the canonical separable GP regression model commonly used in this setting. Our contribution focuses on drastically simplifying, reinterpreting, and then generalizing a recently proposed fully Bayesian GP-SIM combination. Favorable performance is illustrated on synthetic data and a real-data computer experiment. Two R packages, both released on CRAN, have been augmented to facilitate inference under our proposed model(s).
URI: https://hdl.handle.net/10356/94738
http://hdl.handle.net/10220/11876
DOI: http://dx.doi.org/10.1080/00401706.2012.650527
Rights: © 2012 American Statistical Association and the American Society for Quality.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

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