Generalized spatial regression with differential regularization |
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Authors: | Matthieu Wilhelm |
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Institution: | Institut de Statistique, Université de Neuchatel, Neuchatel, Switzerland |
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Abstract: | ABSTRACTWe aim at analysing 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, OR, USA. |
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Keywords: | Functional data analysis spatial data analysis generalized additive model differential regularizations finite element method |
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