Bayesian geostatistical modelling with informative sampling locations.
Journal Article (Journal Article)
We consider geostatistical models that allow the locations at which data are collected to be informative about the outcomes. A Bayesian approach is proposed, which models the locations using a log Gaussian Cox process, while modelling the outcomes conditionally on the locations as Gaussian with a Gaussian process spatial random effect and adjustment for the location intensity process. We prove posterior propriety under an improper prior on the parameter controlling the degree of informative sampling, demonstrating that the data are informative. In addition, we show that the density of the locations and mean function of the outcome process can be estimated consistently under mild assumptions. The methods show significant evidence of informative sampling when applied to ozone data over Eastern U.S.A.
Full Text
Duke Authors
Cited Authors
- Pati, D; Reich, BJ; Dunson, DB
Published Date
- March 2011
Published In
Volume / Issue
- 98 / 1
Start / End Page
- 35 - 48
PubMed ID
- 23956461
Pubmed Central ID
- PMC3744635
Electronic International Standard Serial Number (EISSN)
- 1464-3510
International Standard Serial Number (ISSN)
- 0006-3444
Digital Object Identifier (DOI)
- 10.1093/biomet/asq067
Language
- eng