Dirichlet based Bayesian multivariate receptor modeling

Environmetrics

Volumn 19, Issue 6, 2008, Pages 618-629

  Lingwall, Jeff W ^a   Christensen, William F ^b   Reese, C Shane ^b  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b BRIGHAM YOUNG UNIVERSITY   (United States)

Author keywords

Air pollution; Bayesian methods; Chemical mass balance; Pollution source apportionment; Source attribution

Indexed keywords

ATMOSPHERIC MODELING; ATMOSPHERIC POLLUTION; BAYESIAN ANALYSIS; CHEMICAL MASS BALANCE; COMPARATIVE STUDY; CONCENTRATION (COMPOSITION); MULTIVARIATE ANALYSIS; PARTICULATE MATTER; POLLUTANT SOURCE; STATISTICAL DISTRIBUTION;

DISTRICT OF COLUMBIA; NORTH AMERICA; UNITED STATES; WASHINGTON [DISTRICT OF COLUMBIA];

EID: 52649134053     PISSN: 11804009     EISSN: 1099095X     Source Type: Journal    
DOI: 10.1002/env.902     Document Type: Article

References (21)

1. Christensen WF, Schauer JJ, Lingwall JW. 2006. Iterated confirmatory factor analysis for pollution source apportionment. Environmetrics 17: 663-681.
2. Dockery DW, Pope CA, Xu X, Spengler JD, Ware JH, Fay ME, Ferris BG, Speizer FE. 1993. An association between air pollution and Mortality in six U.S. cities. The New England Journal of Medicine 329: 1753-1759.
3. Dominici F, Samet JM, Zeger SL. 2000. Combining evidence on air pollution and daily mortality from the 20 largest US cities: a hierarchical modelling strategy. Journal of the Royal Statistical Society. Series A (Statistics in Society) 163: 263-302.
4. Dunson DB. 2000. Bayesian latent variable analysis for clustered mixed outcomes. Journal of the Royal Statistical Society. Series B (Statistical Methodology) 62: 355-366.
5. Eberly S. 2005. EPA PMF 1.1 User's Guide. U.S. Environmental Protection Agency: Research Triangle Park, NC.
6. Evans MJ, Gilula Z, Guttman I. 1989. Latent class analysis of two-way contingency tables by Bayesian methods. Biometrika 76: 557-563.
7. Gelman A, Carlin JB, Stern HS, Rubin DB. 2004. Bayesian Data Analysis (2nd edn). Chapman & Hall/CRC: Washington DC.
8. Hopke PK. 1988. Target transformation factor analysis as an aerosol mass apportionment method: a review and sensitivity study. Atmospheric Environment 22: 1777-1792.
9. Ichir MM, Mohammad-Djafari A. 2004. Bayesian blind source separation of positive non-stationary sources. In American Institute of Physics Conference Proceedings: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Vol. 735; 493-500.
10. Knuth KH. 1998. Bayesian source separation and localization. In SPIE'98 Proceedings: Bayesian Inference for Inverse Problems, Mohammad-Djafari A (ed.). San Diego, CA; 147-158.
11. Knuth KH. 1999. A Bayesian approach to source separation. In Proceedings of the First International Workshop on Independent Component Analysis and Signal Separation: ICA'99, Cardoso JF, Jutten C, Loubaton P (eds). Aussois, France; 283-288.
12. Knuth KH, Vaughan HG, Jr. 1999. Convergent Bayesian formulations of blind source separation and electromagnetic source estimation. In Maximum Entropy and Bayesian Methods, Munich 1998, von der Linden W, Dose V, Fischer R, Preuss R. (eds). Dordrecht: Kluwer; 217-226.
13. 2.5 at the St. Louis-Midwest Supersite. Journal of Geophysical Research 111: D10S10. DOI:10.1029/2005JD006329
14. Miskin J, MacKay D. 2001. Ensemble learning for blind source separation. In Independent Component Analysis: Principles and Practice, Roberts S, Everson R (eds). Cambridge University Press: Cambridge, UK; 209-233.
15. Moussaoui S, Brie D, Caspary O, Mohammad-Djafari A. 2004. A Bayesian method for positive source separation. In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'04), Vol. 5; 485-488.
16. Paatero P. 2003. User's Guide for Positive Matrix Factorization Programs PMF2 and PMF3, Part 1: Tutorial. University of Helsinki: Helsinki, Finland.
17. Paatero P, Tapper U. 1994. Positive matrix factorization: A non-negative factor model with optimal utilization of error estimate of data values. (Environmentrics) 5: 111-126.
18. Park ES, Guttorp P, Henry RC. 2001. Multivariate receptor modeling for temporally correlated data by using MCMC. Journal of the American Statistical Association 96: 1171-1183.
19. Park ES, Oh MS, Guttorp P. 2002. Multivariate receptor modeling and model uncertainty. Chemometrics and Intelligent Laboratory Systems 60: 49-67.
20. Rogers GS, Young DL. 1973. On the products of powers of generalized Dirichlet components with an application. The Canadian Journal of Statistics 1: 159-169.
21. Rowe DB. 2003. Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing. Chapman & Hall/CRC: Boca Raton, Florida.