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

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. A computationally efficient implementation, exploiting the sparsity of the arising design and penalty matrices, is proposed. The findings are illustrated using a simulation and two examples, all concerned with the modelling of contaminants in groundwater, which suggest that the proposed strategy is more stable that competing strategies based on the use of criteria such as GCV and AIC.

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