Now showing items 1-5 of 5
Spatio-temporal adaptive penalized splines with application to Neuroscience
Data analysed here derive from experiments conducted to study neurons' activity in the visual cortex of behaving monkeys. We consider a spatio-temporal adaptive penalized spline (P-spline) approach for modelling the firing ...
Smooth additive mixed models for predicting aboveground biomass
Aboveground biomass estimation in short-rotation forestry plantations is an essential step in the development of crop management strategies as well as allowing the economic viability of the crop to be determined prior to ...
Fast estimation of multidimensional adaptive P-spline models
A fast and stable algorithm for estimating multidimensional adaptive P-spline models is presented. We call it as Separation of Overlapping Penalties (SOP) as it is an extension of the Separation of Anisotropic Penalties ...
Modelling latent trends from spatio-temporally grouped data using composite link mixed models
Epidemiological data are frequently recorded at coarse spatio-temporal resolutions. The aggregation process is done for several reasons: to protect confidential patients' information, to compare with other datasets at a ...
Penalized composite link models for aggregated spatial count data: a mixed model approach
Mortality data provide valuable information for the study of the spatial distri- bution of mortality risk, in disciplines such as spatial epidemiology and public health. However, they are frequently available in an aggregated ...