Efficient and automatic methods for flexible regression on spatiotemporal data, with applications to groundwater monitoring

Evers, L. , Molinari, D.A., Bowman, A.W. , Jones, W.R. and Spence, M.J. (2015) Efficient and automatic methods for flexible regression on spatiotemporal data, with applications to groundwater monitoring. Environmetrics, 26(6), pp. 431-441. (doi: 10.1002/env.2347)

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Abstract

Fitting statistical models to spatiotemporal data requires finding the right balance between imposing smoothness and following the data. In the context of P-splines, we propose a Bayesian framework for choosing the smoothing parameter which allows the construction of fully-automatic data-driven methods for fitting flexible models to spatiotemporal data. An implementation, which is highly computationally efficient and which exploits the sparsity of the design and penalty matrices, is proposed. The findings are illustrated using a simulation study and two examples, all concerned with the modelling of contaminants in groundwater. This suggests that the proposed strategy is more stable that competing methods based on the use of criteria such as GCV and AIC.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Jones, Dr Wayne and Evers, Dr Ludger and Bowman, Professor Adrian
Authors: Evers, L., Molinari, D.A., Bowman, A.W., Jones, W.R., and Spence, M.J.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Environmetrics
Publisher:Wiley
ISSN:1180-4009
ISSN (Online):1099-095X
Published Online:18 June 2015
Copyright Holders:Copyright © 2016 The Authors
First Published:First published in Environmetrics 26(6):431-441
Publisher Policy:Reproduced under a Creative Commons License

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