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)

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, Prof 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