Math @ Duke
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Publications [#257967] of David B. Dunson
search arxiv.org.Papers Published
- Page, GL; Dunson, DB, Bayesian Local Contamination Models for Multivariate Outliers.,
Technometrics : a journal of statistics for the physical, chemical, and engineering sciences, vol. 53 no. 2
(May, 2011),
pp. 152-162, ISSN 0040-1706 [doi]
(last updated on 2025/04/11)
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.
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