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Generalized Spatial Regression with Differential Regularization
Auteur(s)
Sangalli, Laura M.
Date de parution
2016-5-10
In
Journal of Statistical Computation and Simulation
Vol.
13
No
86
De la page
2497
A la page
2518
Revu par les pairs
1
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.
Identifiants
Type de publication
journal article
