Bayesian Local Contamination Models for Multivariate Outliers.

Published

Journal Article

In studies where data are generated from multiple locations or sources it is common for there to exist observations that are quite unlike the majority. Motivated by the application of establishing a reference value in an inter-laboratory setting when outlying labs are present, we propose a local contamination model that is able to accommodate unusual multivariate realizations in a flexible way. The proposed method models the process level of a hierarchical model using a mixture with a parametric component and a possibly nonparametric contamination. Much of the flexibility in the methodology is achieved by allowing varying random subsets of the elements in the lab-specific mean vectors to be allocated to the contamination component. Computational methods are developed and the methodology is compared to three other possible approaches using a simulation study. We apply the proposed method to a NIST/NOAA sponsored inter-laboratory study which motivated the methodological development.

Full Text

Duke Authors

Cited Authors

  • Page, GL; Dunson, DB

Published Date

  • May 2011

Published In

Volume / Issue

  • 53 / 2

PubMed ID

  • 24363465

Pubmed Central ID

  • 24363465

International Standard Serial Number (ISSN)

  • 0040-1706

Digital Object Identifier (DOI)

  • 10.1198/tech.2011.10041

Language

  • eng