A model for spatio-temporal clustering using multinomial probit regression: Application to avalanche counts

Environmetrics

Volumn 23, Issue 6, 2012, Pages 522-534

  Lavigne, A ^a   Bel, L ^a   Parent, E ^a   Eckert, N ^b  

^a AgroParisTech   (France)

^b Cemagref   (France)

Author keywords

Avalanche counts; Bayesian paradigm; Multinomial probit regression; Spatio temporal modeling

Indexed keywords

AVALANCHE; BAYESIAN ANALYSIS; CLIMATE CHANGE; COVARIANCE ANALYSIS; DATA SET; HIERARCHICAL SYSTEM; PARADIGM SHIFT; REGIONAL CLIMATE; REGRESSION ANALYSIS; SPATIOTEMPORAL ANALYSIS;

ALPS; FRANCE;

EID: 84866621508     PISSN: 11804009     EISSN: 1099095X     Source Type: Journal    
DOI: 10.1002/env.2167     Document Type: Article

References (33)

1. Albert JH, Chib S. 1993.Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association 88(422): 669-679.
2. Ambroise C, Dang M, Govaert G. 1997. Clustering of spatial data by the EM algorithm. geoENV I-Geostatistics for Environmental Applications 9: 493-504.
3. Besag J, Kooperberg C. 1995. On conditional and intrinsic autoregressions. Biometrika 82(4): 733-746.
4. Besag J, York J, Mollié A. 1991. Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics 43(1): 1-59.
5. Castebrunet H, Eckert N, Giraud G. 2012. Snow and weather climatic control on snow avalanche occurrence fluctuations over 50years in the French Alps. Climate of the Past 8: 855-875.
6. Celeux G, Forbes F, Robert CP, Titterington DM. 2006. Deviance information criteria for missing data models. Bayesian Analysis 1(4): 651-674.
7. Cressie N, Wikle CK. 2011. Statistics for Spatio-Temporal Data, Vol. 465. Wiley: Hoboken, New Jersey.
8. Durand Y, Laternser M, Giraud G, Etchevers P, Lesaffre B, Mérindol L. 2009a. Reanalysis of 44 yr of climate in the French Alps (1958-2002): methodology, model validation, climatology, and trends for air temperature and precipitation. Journal of Applied Meteorology and Climatology 48(3): 429-449.
9. Durand Y, Giraud G, Laternser M, Etchevers P, Mérindol L, Lesaffre B. 2009b. Reanalysis of 47years of climate in the French Alps (1958-2005): climatology and trends for snow cover. Journal of Applied Meteorology and Climatology 48(12): 2487-2512.
10. Eckert N, Baya H, Deschatres M. 2010. Assessing the response of snow avalanche runout altitudes to climate fluctuations using hierarchical modeling: application to 61 winters of data in France. Journal of Climate 23(12): 3157-3180.
11. Eckert N, Baya H, Thibert E, Vincent C. 2011. Extracting the temporal signal from a winter and summer mass-balance series: application to a six-decade record at Glacier de Sarennes, French Alps. Journal of Glaciology 57(201): 134-150.
12. Eckert N, Keylock CJ, Castebrunet H, Lavigne A, Naaim M.Temporal trends in avalanche activity in the French Alps and subregions: from occurrences and runout altitudes to unsteady return periods. submitted.
13. Eckert N, Parent E, Belanger L, Garcia S. 2007. Hierarchical Bayesian modelling for spatial analysis of the number of avalanche occurrences at the scale of the township. Cold Regions Science and Technology 50(1-3): 97-112.
14. Eckert N, Parent E, Kies R, Baya H. 2010b. A spatio-temporal modelling framework for assessing the fluctuations of avalanche occurrence resulting from climate change: application to 60years of data in the northern French Alps. Climatic change 101(3): 515-553.
15. Elliott P, Wartenberg D. 2004. Spatial epidemiology: current approaches and future challenges. Environmental Health Perspectives 112(9): 998.
16. Gelman A, Carlin JB, Stern HS, Rubin DB. 2004.Bayesian Data Analysis. Chapman & Hall/CRC: Boca Raton, Florida, ISBN 158488388X.
17. Gelfand AE, Smith AFM. 1990.Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association: 398-409.
18. Gilks WR, Wild P. 1992. Adaptive rejection sampling for Gibbs sampling. Applied Statistics 41(2): 337-348.
19. Gneiting T, Raftery AE. 2007. Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association 102(477): 359-378.
20. Guin O, Naveau P, Boreux J.Extracting hidden trends in tree-rings with a semi-parametric Bayesian hierarchical model. submitted.
21. Hathaway RJ. 1986. Another interpretation of the EM algorithm for mixture distributions. Statistics & Probability Letters 4(2): 53-56.
22. Hilasvuori E, Berninger F, Sonninen E, Tuomenvirta H, Jungner H. 2009. Stability of climate signal in carbon and oxygen isotope records and ring width from Scots pine (Pinus sylvestris l.) in Finland. Journal of Quaternary Science 24(5): 469-480.
23. López-Moreno JI, Goyette S, Vicente-Serrano SM, Beniston M. 2011. Effects of climate change on the intensity and frequency of heavy snowfall events in the Pyrenees. Climatic change 374(3): 1-20.
24. McCulloch R, Rossi PE. 1994. An exact likelihood analysis of the multinomial probit model. Journal of Econometrics 64(1): 207-240.
25. Plummer M. 2008. Penalized loss functions for Bayesian model comparison. Biostatistics 9(3): 523-539.
26. Reich BJ, Bondell HD. 2011. A spatial Dirichlet process mixture model for clustering population genetics data. Biometrics 67(2): 381-390.
27. Robert CP, Casella G. 2004. Monte Carlo Statistical Methods. Springer Verlag: New York. ISBN 0387212396.
28. R Core Team. 2012. R: a language and environment for statistical computing. R Foundation for Statistical Computing: Vienna, Austria. URL http://www.R-project.org ISBN 3-900051-07-0.
29. Speckman PL, Sun D. 2003. Fully Bayesian spline smoothing and intrinsic autoregressive priors. Biometrika 90(2): 289-302.
30. Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A. 2002. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society. Series B, Statistical Methodology 64(4): 583-639.
31. Wahba G. 1978. Improper priors, spline smoothing and the problem of guarding against model errors in regression. Journal of the Royal Statistical Society. Series B (Methodological) 40(3): 364-372.
32. Wikle CK. 2002 A kernel-based spectral model for non-Gaussian spatio-temporal processes. Statistical Modelling 2(4): 299.
33. Rue H, Held L. 2005. Gaussian Markov Random Fields: Theory and Applications, Monographs on Statistics and Applied Probability, Vol. 104. Chapman & Hall: London.