RT Article
T1 Generalized Spatial Regression with Differential Regularization
JF The journal of statistical computation and simulation
A1 Sangalli, Laura M.
A2 Wilhelm, Matthieu
LA English
YR 2016
UL https://krimdok.uni-tuebingen.de/Record/1865846082
AB We aim at analyzing geostatistical and areal data observed over irregularly shaped spatial domains and having a distribution within the exponential family. We propose a generalized additive model that allows to account for spatially-varying covariate information. The model is fitted by maximizing a penalized log-likelihood function, with a roughness penalty term that involves a differential quantity of the spatial field, computed over the domain of interest. Efficient estimation of the spatial field is achieved resorting to the finite element method, which provides a basis for piecewise polynomial surfaces. The proposed model is illustrated by an application to the study of criminality in the city of Portland, Oregon, USA
K1 Research
DO 10.1080/00949655.2016.1182532