Generalized Spatial Regression with Differential Regularization
Methodology
2016-04-22 v2
Abstract
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.
Cite
@article{arxiv.1511.02688,
title = {Generalized Spatial Regression with Differential Regularization},
author = {Matthieu Wilhelm and Laura M. Sangalli},
journal= {arXiv preprint arXiv:1511.02688},
year = {2016}
}