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Generalized Spatial Regression with Differential Regularization

Matthieu Wilhelm & Laura M. Sangalli

Résumé We propose a method for the analysis of data scattered over a spatial irregularly shaped domain and having a distribution within the exponential family. This is a generalized additive model for spatially distributed data. The model is fitted by maximizing a penalized log-likelihood function with a roughness penalty term that involves a differential operator of the spatial field over the domain of interest. Efficient spatial field estimation is achieved resorting to the finite element method, which provides a basis for piecewise polynomial surfaces. The method is illustrated by an application to the study of criminality in the city of Portland, Oregon, USA.
   
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Citation Wilhelm, M., & Sangalli, L. M. (2016). Generalized Spatial Regression with Differential Regularization. Journal of Statistical Computation and Simulation, 86(13), 2497-2518.
   
Type Article de périodique (Anglais)
Date de publication 10-5-2016
Nom du périodique Journal of Statistical Computation and Simulation
Volume 86
Numéro 13
Pages 2497-2518
URL http://www.tandfonline.com/doi/full/10.1080/00949655.2016...