Spatio-temporal modeling of particulate matter concentration through the SPDE approach

AStA Advances in Statistical Analysis

Volumn 97, Issue 2, 2013, Pages 109-131

  Cameletti, Michela ^a   Lindgren, Finn ^b   Simpson, Daniel ^b   Rue, Håvard ^b  

^a UNIVERSITY OF BERGAMO   (Italy)

^b NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

Author keywords

Covariance functions; Gaussian fields; Gaussian Markov random fields; Hierarchical models; Integrated Nested Laplace Approximation

Indexed keywords

EID: 84875739913     PISSN: 18638171     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10182-012-0196-3     Document Type: Article

References (19)

1. Banerjee, S., Carlin, B., Gelfand, A.: Hierarchical Modeling and Analysis for Spatial Data. Monographs on Statistics and Applied Probability. Chapman and Hall, New York (2004).
2. Banerjee, S., Gelfand, A., Finley, A., Sang, H.: Gaussian predictive process models for large spatial datasets. J. R. Stat. Soc. B 70(4), 825-848 (2008).
3. Besag, J.: Spatial interaction and the statistical analysis of lattice systems (with discussion). J. R. Stat. Soc. B 36(2), 192-225 (1974).
4. Cameletti, M., Ignaccolo, R., Bande, S.: Comparing spatio-temporal models for particulate matter in Piemonte. Environmetrics (2011). doi: 10. 1002/env. 1139.
5. 10 pollution. Atmos. Environ. 41, 532-542 (2007).
6. Cressie, N.: Statistics for Spatial Data. Wiley, New York (1993).
7. Cressie, N., Johannesson, G.: Fixed rank kriging for large spatial datasets. J. R. Stat. Soc. B 70, 209-226 (2008).
8. Cressie, N., Wikle, C.: Statistics for Spatio-Temporal Data. Wiley, New York (2011).
9. Fassò, A., Finazzi, F.: Maximum likelihood estimation of the dynamic coregionalization model with heterotopic data. Environmetrics 22, 735-748 (2011).
10. Finardi, S., De Maria, R., D'Allura, A., Cascone, C., Calori, G., Lollobrigida, F.: A deterministic air quality forecasting system for Torino urban area, Italy. Environ. Model. Softw. 23(3), 344-355 (2008).
11. Furrer, R., Genton, M., Nychka, D.: Covariance tapering for interpolation of large spatial datasets. J. Comput. Graph. Stat. 15(3), 502-523 (2006).
12. Gelfand, A., Diggle, P., Fuentes, M., Guttorp, P. (eds.): Handbook of Spatial Statistics. Chapman & Hall, New York (2010).
13. Lindgren, F., Rue, H., Lindström, J.: An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach (with discussion). J. R. Stat. Soc. B 73(4), 423-498 (2011).
14. R Development Core Team: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2011). URL http://www. R-project. org, ISBN 3-900051-07-0.
15. Rue, H., Held, L.: Gaussian Markov Random Fields. Theory and Applications. Chapman & Hall, New York (2005).
16. Rue, H., Martino, S., Chopin, N.: Approximate Bayesian inference for latent Gaussian model by using integrated nested Laplace approximations (with discussion). J. R. Stat. Soc. B 71, 319-392 (2009).
17. Sahu, S.: Hierarchical Bayesian Models for Space-time Air Pollution Data. Rao, C. (ed.): Handbook of Statistics-Time Series Analysis, Methods and Applications. Handbook of Statistics, vol. 30. Elsevier, Amsterdam (2011).
18. Samet, J., Dominici, F., Curriero, F., Coursac, I., Zeger, S.: Fine particulate air pollution and mortality in 20 US cities: 1987-1994. N. Engl. J. Med. 343, 1742-1749 (2000).
19. Samoli, E., Peng, R., Ramsay, T., Pipikou, M., Touloumi, G., Dominici, F., Burnett, R., Cohen, A., Krewski, D., Samet, J., Katsouyanni, K.: Acute effects of ambient particulate matter on mortality in Europe and North America: results from the APHENA study. Environ. Health Perspect. 116, 1480-1486 (2008).