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Efficient and automatic methods for flexible regression on spatiotemporal data, with applications to groundwater monitoring
Author(s) -
Evers L.,
Molinari D. A.,
Bowman A. W.,
Jones W. R.,
Spence M. J.
Publication year - 2015
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2347
Subject(s) - akaike information criterion , smoothing , computer science , data mining , smoothness , context (archaeology) , exploit , regression , bayesian information criterion , bayesian probability , machine learning , artificial intelligence , statistics , mathematics , mathematical analysis , paleontology , biology , computer vision , computer security
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 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 generalised cross‐validation and Akaike's Information Criterion. © 2015 The Authors. Environmetrics Published by John Wiley Sons Ltd.