Hierarchical Modeling for Spatial Data Problems.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Gelfand, AE

Published Date

  • May 2012

Published In

Volume / Issue

  • 1 /

Start / End Page

  • 30 - 39

PubMed ID

  • 24010050

Pubmed Central ID

  • PMC3760588

Electronic International Standard Serial Number (EISSN)

  • 2211-6753

International Standard Serial Number (ISSN)

  • 2211-6753

Digital Object Identifier (DOI)

  • 10.1016/j.spasta.2012.02.005


  • eng