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Multivariate receptor models and model uncertainty

Title
Multivariate receptor models and model uncertainty
Authors
Park E.S.Oh M.-S.Guttorp P.
Ewha Authors
오만숙
SCOPUS Author ID
오만숙scopus
Issue Date
2002
Journal Title
Chemometrics and Intelligent Laboratory Systems
ISSN
0169-7439JCR Link
Citation
vol. 60, no. 1-2, pp. 49 - 67
Indexed
SCI; SCIE; SCOPUS WOS scopus
Abstract
Estimation of the number of major pollution sources, the source composition profiles, and the source contributions are the main interests in multivariate receptor modeling. Due to lack of identifiability of the receptor model, however, the estimation cannot be done without some additional assumptions. A common approach to this problem is to estimate the number of sources, q, at the first stage, and then estimate source profiles and contributions at the second stage, given additional constraints (identifiability conditions) to prevent source rotation/transformation and the assumption that the q-source model is correct. These assumptions on the parameters (the number of sources and identifiability conditions) are the main source of model uncertainty in multivariate receptor modeling. In this paper, we suggest a Bayesian approach to deal with model uncertainties in multivariate receptor models by using Markov chain Monte Carlo (MCMC) schemes. Specifically, we suggest a method which can simultaneously estimate parameters (compositions and contributions), parameter uncertainties, and model uncertainties (number of sources and identifiability conditions). Simulation results and an application to air pollution data are presented. © 2002 Elsevier Science B.V. All rights reserved.
DOI
10.1016/S0169-7439(01)00185-X
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자연과학대학 > 통계학전공 > Journal papers
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