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Non-Gaussian spatiotemporal modelling through scale mixing
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Fonseca, Thaís C. O. and Steel, Mark F. J. (2011) Non-Gaussian spatiotemporal modelling through scale mixing. Biometrika, Vol.98 (No.4). pp. 761-774. doi:10.1093/biomet/asr047 ISSN 0006-3444.
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Official URL: http://dx.doi.org/10.1093/biomet/asr047
Abstract
We construct non-Gaussian processes that vary continuously in space and time with nonseparable covariance functions. Starting from a general and flexible way of constructing valid nonseparable covariance functions through mixing over separable covariance functions, the resulting models are generalized by allowing for outliers as well as regions with larger variances. We induce this through scale mixing with separate positive-valued processes. Smooth mixing processes are applied to the underlying correlated processes in space and in time, thus leading to regions in space and time of increased spread. An uncorrelated mixing process on the nugget effect accommodates outliers. Posterior and predictive Bayesian inference with these models is implemented through a Markov chain Monte Carlo sampler. An application to temperature data in the Basque country illustrates the potential of this model in the identification of outliers and regions with inflated variance, and shows that this improves the predictive performance.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Library of Congress Subject Headings (LCSH): | Bayesian statistical decision theory, Gaussian processes, Outliers (Statistics) | ||||
Journal or Publication Title: | Biometrika | ||||
Publisher: | Biometrika Trust | ||||
ISSN: | 0006-3444 | ||||
Official Date: | 2011 | ||||
Dates: |
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Volume: | Vol.98 | ||||
Number: | No.4 | ||||
Page Range: | pp. 761-774 | ||||
DOI: | 10.1093/biomet/asr047 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access | ||||
Funder: | University of Warwick. Centre for Research in Statistical Methodology |
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