Spatiotemporal modeling of PM2.5 data with missing values

Journal of Geophysical Research: Atmospheres

Volumn 108, Issue 24, 2003, Pages

  Smith, Richard L ^a   Kolenikov, Stanislav ^a   Cox, Lawrence H ^b  

^a University of North Carolina at Chapel Hill   (United States)

^b NATIONAL CENTER FOR HEALTH STATISTICS   (United States)

Author keywords

EPA standards; Geostatistics; Particulate matter; Spatial statistics; Time series

Indexed keywords

ALGORITHM; ATMOSPHERIC POLLUTION; GEOSTATISTICS; INTERPOLATION; PARTICULATE MATTER; SPATIOTEMPORAL ANALYSIS;

NORTH AMERICA; UNITED STATES;

EID: 1642332347     PISSN: 01480227     EISSN: None     Source Type: Journal    
DOI: 10.1029/2002jd002914     Document Type: Article

References (18)

1. Box, G. E. P., and D. R. Cox, An analysis of transformations (with discussion), J. R. Stat. Soc. B, 26, 211-246, 1964.
2. Chilès, J.-P., and P. Delfiner, Geostatistics: Modeling Spatial Uncertainty, John Wiley, Hoboken, N. J., 1999.
3. Cox, L. H., Statistical issues in the study of air pollution involving airborne particulate matter, Environmetrics, 11, 611-626, 2000.
4. Cressie, N., Statistics for Spatial Data, 2nd ed., John Wiley, Hoboken, N. J., 1993.
5. Daniels, M. J., Y.-D. Lee, and M. Kaiser, Assessing sources of variability in measurement of ambient particulate matter, Environmetrics, 12, 547-558, 2001.
6. Dempster, A. P., N. M. Laird, and D. B. Rubin, Maximum likelihood estimation from incomplete data via the EM algorithm (with discussion), J. R. Stat. Soc. B, 39, 1-38, 1977.
7. Green, P. J., and B. W. Silverman, Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach, Chapman and Hall, New York, 1994.
8. Handcock, M. S., and M. Stein, A Bayesian analysis of kriging, Technometrics, 35, 403-410, 1993.
9. Hartigan, J. A., and M. A. Wong, A K-means clustering algorithm, Appl. Stat., 28, 101-108, 1979.
10. Holland, D. M., V. De Oliveira, L. H. Cox, and R. L. Smith, Estimation of regional trends in sulfur dioxide over the eastern United States, Environmetrics, 11, 373-393, 2000.
11. Kitanidis, P. K., Statistical estimation of polynomial generalized covariance functions and hydrologic applications, Water Resour. Res., 19, 909-921, 1983.
12. Little, R. J. A., and D. B. Rubin, Statistical Analysis With Missing Data, John Wiley, Hoboken, N. J., 1987.
13. Matheron, G., The intrinsic random functions and their applications, Adv. Appl. Prob., 5, 439-468, 1973.
14. McLachlan, G. J., and T. Krishnan, The EM Algorithm and Extensions, John Wiley, Hoboken, N. J., 1997.
15. National Research Council, Research Priorities for Airborne Particulate Matter, vol. 1, Immediate Priorities and a Long-Range Research Portfolio, Natl. Acad. Press, Washington, D. C., 1998.
16. StataCorp, Stata Statistical Software: Release 7, College Station, Tex., 2001.
17. Stein, M. L., Interpolation of Spatial Data: Some Theory of Kriging, Springer Verlag, New York, 1999.
18. Zimmerman, D. L., and N. Cressie, Mean squared prediction error in the spatial linear model with estimated covariance parameters, Ann. Inst. Stat. Math., 44, 27-43, 1992.