Generalized Spatial Regression with Differential Regularization

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...

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Autor principal: Sangalli, Laura M. (Autor)
Otros Autores: Wilhelm, Matthieu
Tipo de documento: Electrónico Artículo
Lenguaje:Inglés
Publicado: 2016
En: The journal of statistical computation and simulation
Año: 2016
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Sumario: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
ISSN:1563-5163
DOI:10.1080/00949655.2016.1182532