JSM 2005 - Toronto

Abstract #302490

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 167
Type: Invited
Date/Time: Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract - #302490
Title: Assessment of Model Uncertainty in Multivariate Receptor Modeling
Author(s): Eun Sug Park*+
Companies: Texas Transportation Institute
Address: The Texas A&M University System, College Station, TX, 77845,
Keywords: number of sources ; model identifiability ; marginal likelihood
Abstract:

Identification of pollution sources and quantification of their impacts is a problem of fundamental importance in air pollution studies. Multivariate receptor modeling aims to achieve this goal by unfolding the air pollution data obtained at a receptor into components associated with different sources based on factor analysis models. As in factor analysis models, the unknown number of pollution sources and nonidentifiability of model are the first obstacles we encounter. A common approach to this problem is to determine the number of sources and identifiability conditions first, and make inferences of the remaining model parameters conditionally on that. Such an approach ignores uncertainty in the number of sources and identifiability conditions, which constitute model uncertainty. This paper discusses a Bayesian approach to assess model uncertainty by using marginal likelihood of each model. The estimation of marginal likelihoods that used to be computationally intractable and estimation of parameters and parameter uncertainties are efficiently carried out by the Markov chain Monte Carlo method. The method is illustrated with simulated data and real air pollution data.

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Revised March 2005