Bayesian geostatistical modelling with informative sampling locations.

Published

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

  • 23956461

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