title: Hierarchical Modeling for Spatial Data Problems.
abstract: This short paper is centered on hierarchical modeling for problems in spatial and spatio-temporal statistics. It draws its motivation from the interdisciplinary research work of the author in terms of applications in the environmental sciences - ecological processes, environmental exposure, and weather modeling. The paper briefly reviews hierarchical modeling specification, adopting a Bayesian perspective with full inference and associated uncertainty within the specification, while achieving exact inference to avoid what may be uncomfortable asymptotics. It focuses on point-referenced (geo-statistical) and point pattern spatial settings. It looks in some detail at problems involving data fusion, species distributions, and large spatial datasets. It also briefly describes four further examples arising from the author's recent research projects.
authors.affiliation: Department of Statistical Science, Duke University, Durham, North Carolina, 27708-0251, USA.
authors.forename: Alan E
authors.initials: AE
authors.lastname: Gelfand
keywords: Data fusion; Dirichlet processes; directional data; extreme values; kernel predictors; species distributions