Generalized spatial regression with differential regularization. Issue 13 (1st September 2016)

• Record Type: Journal Article
• Authors: Wilhelm, Matthieu; Sangalli, Laura M.
• Is Part Of: Journal of statistical computation and simulation. Volume 86:Issue 13(2016)
• Page Start: 2497
• Page End: 2518
• Publication Date: 2016-09-01
• Subjects: Functional data analysis -- spatial data analysis -- generalized additive model -- differential regularizations -- finite element method; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2016.1182532
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: We 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.