Overstall, A. M. and Woods, D. C. (2016) Multivariate emulation of computer simulators: model selection and diagnostics with application to a humanitarian relief model. Journal of the Royal Statistical Society: Series C (Applied Statistics), 65(4), pp. 483-505. (doi: 10.1111/rssc.12141)
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Abstract
We present a common framework for Bayesian emulation methodologies for multivariate-output simulators, or computer models, that employ either parametric linear models or nonparametric Gaussian processes. Novel diagnostics suitable for multivariate covariance-separable emulators are developed and techniques to improve the adequacy of an emulator are discussed and implemented. A variety of emulators are compared for a humanitarian relief simulator, modelling aid missions to Sicily after a volcanic eruption and earthquake, and a sensitivity analysis is conducted to determine the sensitivity of the simulator output to changes in the input variables. The results from parametric and nonparametric emulators are compared in terms of prediction accuracy, uncertainty quantification and scientific interpretability.
| Item Type: | Articles |
|---|---|
| Additional Information: | This article was supported by funding to University of Southampton from EPSRC EP/J018317/1. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Overstall, Dr Antony |
| Authors: | Overstall, A. M., and Woods, D. C. |
| College/School: | College of Science and Engineering > School of Mathematics and Statistics > Statistics |
| Journal Name: | Journal of the Royal Statistical Society: Series C (Applied Statistics) |
| Publisher: | Wiley-Blackwell Publishing Ltd. |
| ISSN: | 0035-9254 |
| ISSN (Online): | 1467-9876 |
| Published Online: | 01 March 2016 |
| Copyright Holders: | Copyright © 2016 The Authors |
| First Published: | First published in Journal of the Royal Statistical Society: Series C (Applied Statistics) 2016 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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