TY - JOUR
T1 - Multivariate receptor models and model uncertainty
AU - Park, Eun Sug
AU - Oh, Man Suk
AU - Guttorp, Peter
N1 - Funding Information:
Although the research described in this article has been funded by the United States Environmental Protection Agency through agreement CR825173-01-0 to the University of Washington, it has not been subjected to the agency's required peer and policy review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred.
PY - 2002/1/28
Y1 - 2002/1/28
N2 - 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.
AB - 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.
KW - Factor analysis models
KW - Latent variable models
KW - Marginal likelihood
KW - Model identifiability
KW - Model uncertainty
KW - Number of sources
KW - Posterior model probability
UR - http://www.scopus.com/inward/record.url?scp=0037185404&partnerID=8YFLogxK
U2 - 10.1016/S0169-7439(01)00185-X
DO - 10.1016/S0169-7439(01)00185-X
M3 - Article
AN - SCOPUS:0037185404
SN - 0169-7439
VL - 60
SP - 49
EP - 67
JO - Chemometrics and Intelligent Laboratory Systems
JF - Chemometrics and Intelligent Laboratory Systems
IS - 1-2
ER -