title: Bayesian Local Contamination Models for Multivariate Outliers.
abstract: 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.
authors.affiliation: Department of Statistical Science, Duke University, Durham, NC 27704.
authors.forename: Garritt L
authors.initials: GL
authors.lastname: Page
authors.affiliation: Department of Statistical Science, Duke University, Durham, NC 27704.
authors.forename: David B
authors.initials: DB
authors.lastname: Dunson
keywords: Bayesian robustness; Component-wise classification; Inter-laboratory studies; Mixtures