Variable Selection for Gaussian Process Models using Experimental Design-Based Subagging

DOI10.5705/ss.202016.0250zbMath1394.62108OpenAlexW2607073553MaRDI QIDQ4571217

Yasuo Amemiya, Ying Hung, Yi-Bo Zhao

Publication date: 6 July 2018

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.5705/ss.202016.0250

zbMATH Keywords

model selectionGaussian processbaggingexperimental designcomputer experimentLatin hypercube design

Mathematics Subject Classification ID

Gaussian processes (60G15) Statistical block designs (62K10)

Related Items (6)

An Approach to Incorporate Subsampling Into a Generic Bayesian Hierarchical Model ⋮ Gaussian Process Prediction using Design-Based Subsampling ⋮ Information-based optimal subdata selection for non-linear models ⋮ Predictive Subdata Selection for Computer Models ⋮ Indicator-based Bayesian variable selection for Gaussian process models in computer experiments ⋮ Model-robust subdata selection for big data

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