Overdispersion: models and estimation
Computational Statistics & Data Analysis
Direct generalized additive modeling with penalized likelihood
Computational Statistics & Data Analysis
Mixtures of spatial and unstructured effects for spatially discontinuous health outcomes
Computational Statistics & Data Analysis
Fast and compact smoothing on large multidimensional grids
Computational Statistics & Data Analysis
A model for non-parametric spatially varying regression effects
Computational Statistics & Data Analysis
Editorial: Spatial statistics: Methods, models & computation
Computational Statistics & Data Analysis
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Penalized splines (P-splines) and individual random effects are used for the analysis of spatial count data. P-splines are represented as mixed models to give a unified approach to the model estimation procedure. First, a model where the spatial variation is modelled by a two-dimensional P-spline at the centroids of the areas or regions is considered. In addition, individual area-effects are incorporated as random effects to account for individual variation among regions. Finally, the model is extended by considering a conditional autoregressive (CAR) structure for the random effects, these are the so called ''Smooth-CAR'' models, with the aim of separating the large-scale geographical trend, and local spatial correlation. The methodology proposed is applied to the analysis of lip cancer incidence rates in Scotland.