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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| Format: | Electronic Article |
| Language: | English |
| Published: |
2016
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| In: |
The journal of statistical computation and simulation
Year: 2016 |
| Online Access: |
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| Journals Online & Print: | Drawer...
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| Check availability: | HBZ Gateway |
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| Summary: | 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 |
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| ISSN: | 1563-5163 |
| DOI: | 10.1080/00949655.2016.1182532 |