Statistical Science
- Statist. Sci.
- Volume 27, Number 2 (2012), 161-186.
Statistical Modeling of Spatial Extremes
A. C. Davison, S. A. Padoan, and M. Ribatet
Full-text: Open access
Abstract
The areal modeling of the extremes of a natural process such as rainfall or temperature is important in environmental statistics; for example, understanding extreme areal rainfall is crucial in flood protection. This article reviews recent progress in the statistical modeling of spatial extremes, starting with sketches of the necessary elements of extreme value statistics and geostatistics. The main types of statistical models thus far proposed, based on latent variables, on copulas and on spatial max-stable processes, are described and then are compared by application to a data set on rainfall in Switzerland. Whereas latent variable modeling allows a better fit to marginal distributions, it fits the joint distributions of extremes poorly, so appropriately-chosen copula or max-stable models seem essential for successful spatial modeling of extremes.
Article information
Source
Statist. Sci., Volume 27, Number 2 (2012), 161-186.
Dates
First available in Project Euclid: 19 June 2012
Permanent link to this document
https://projecteuclid.org/euclid.ss/1340110864
Digital Object Identifier
doi:10.1214/11-STS376
Mathematical Reviews number (MathSciNet)
MR2963980
Zentralblatt MATH identifier
1330.86021
Keywords
Annual maximum analysis Bayesian hierarchical model Brown–Resnick process composite likelihood copula environmental data analysis Gaussian process generalized extreme-value distribution geostatistics latent variable max-stable process statistics of extremes
Citation
Davison, A. C.; Padoan, S. A.; Ribatet, M. Statistical Modeling of Spatial Extremes. Statist. Sci. 27 (2012), no. 2, 161--186. doi:10.1214/11-STS376. https://projecteuclid.org/euclid.ss/1340110864
References
- Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In Second International Symposium on Information Theory (Tsahkadsor, 1971) (B. N. Petrov and F Czáki, eds.) 267–281. Akadémiai Kiadó, Budapest.
- Banerjee, S., Carlin, B. P. and Gelfand, A. E. (2004). Hierarchical Modeling and Analysis for Spatial Data. Chapman & Hall/CRC, New York.Zentralblatt MATH: 1053.62105
- Beirlant, J., Goegebeur, Y., Teugels, J. and Segers, J. (2004). Statistics of Extremes: Theory and Applications. Wiley, Chichester.
- Bevilacqua, M., Gaetan, C., Mateu, J. and Porcu, E. (2012). Estimating space and space–time covariance functions: A weighted composite likelihood approach. J. Amer. Statist. Assoc. 107. To appear.
- Blanchet, J. and Davison, A. C. (2011). Spatial modelling of extreme snow depth. Ann. Appl. Stat. 5 1699–1725.Mathematical Reviews (MathSciNet): MR2884920
Zentralblatt MATH: 1228.62154
Digital Object Identifier: doi:10.1214/11-AOAS464
Project Euclid: euclid.aoas/1318514282 - Boldi, M. O. and Davison, A. C. (2007). A mixture model for multivariate extremes. J. R. Stat. Soc. Ser. B Stat. Methodol. 69 217–229.Mathematical Reviews (MathSciNet): MR2325273
Zentralblatt MATH: 1120.62030
Digital Object Identifier: doi:10.1111/j.1467-9868.2007.00585.x - Buishand, T. A., de Haan, L. and Zhou, C. (2008). On spatial extremes: With application to a rainfall problem. Ann. Appl. Stat. 2 624–642.Mathematical Reviews (MathSciNet): MR2524349
Zentralblatt MATH: 05591291
Digital Object Identifier: doi:10.1214/08-AOAS159
Project Euclid: euclid.aoas/1215118531 - Butler, A., Heffernan, J. E., Tawn, J. A. and Flather, R. A. (2007). Trend estimation in extremes of synthetic North Sea surges. J. Roy. Statist. Soc. Ser. C 56 395–414.Mathematical Reviews (MathSciNet): MR2409758
Digital Object Identifier: doi:10.1111/j.1467-9876.2007.00583.x - Casson, E. and Coles, S. (1999). Spatial regression models for extremes. Extremes 1 449–468.Zentralblatt MATH: 0935.62109
- Chavez-Demoulin, V. and Davison, A. C. (2005). Generalized additive modelling of sample extremes. J. Roy. Statist. Soc. Ser. C 54 207–222.Mathematical Reviews (MathSciNet): MR2134607
Zentralblatt MATH: 05188681
Digital Object Identifier: doi:10.1111/j.1467-9876.2005.00479.x - Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer, London.
- Coles, S. G. and Casson, E. (1998). Extreme value modelling of hurricane wind speeds. Structural Safety 20 283–296.
- Coles, S. G. and Tawn, J. A. (1991). Modelling extreme multivariate events. J. Roy. Statist. Soc. Ser. B 53 377–392.Mathematical Reviews (MathSciNet): MR1108334
- Cooley, D. and Sain, S. R. (2010). Spatial hierarchical modeling of precipitation extremes from a regional climate model. J. Agric. Biol. Environ. Stat. 15 381–402.Mathematical Reviews (MathSciNet): MR2787265
Digital Object Identifier: doi:10.1007/s13253-010-0023-9 - Cooley, D., Naveau, P. and Poncet, P. (2006). Variograms for spatial max-stable random fields. In Dependence in Probability and Statistics. Lecture Notes in Statist. 187 373–390. Springer, New York.Mathematical Reviews (MathSciNet): MR2283264
Zentralblatt MATH: 1110.62130
Digital Object Identifier: doi:10.1007/0-387-36062-X_17 - Cooley, D., Nychka, D. and Naveau, P. (2007). Bayesian spatial modeling of extreme precipitation return levels. J. Amer. Statist. Assoc. 102 824–840.Mathematical Reviews (MathSciNet): MR2411647
Zentralblatt MATH: 05564414
Digital Object Identifier: doi:10.1198/016214506000000780 - Cox, D. R. and Reid, N. (2004). A note on pseudolikelihood constructed from marginal densities. Biometrika 91 729–737.Mathematical Reviews (MathSciNet): MR2090633
Zentralblatt MATH: 1162.62365
Digital Object Identifier: doi:10.1093/biomet/91.3.729 - Cressie, N. A. C. (1993). Statistics for Spatial Data. Wiley, New York.
- Davis, R. and Resnick, S. (1984). Tail estimates motivated by extreme value theory. Ann. Statist. 12 1467–1487.Mathematical Reviews (MathSciNet): MR760700
Zentralblatt MATH: 0555.62035
Digital Object Identifier: doi:10.1214/aos/1176346804
Project Euclid: euclid.aos/1176346804 - Davis, R. A. and Yau, C. Y. (2011). Comments on pairwise likelihood in time series models. Statist. Sinica 21 255–277.
- Davison, A. C. and Gholamrezaee, M. M. (2012). Geostatistics of extremes. Proc. R. Soc. Lond. Ser. A 468 581–608.
- Davison, A. C. and Ramesh, N. I. (2000). Local likelihood smoothing of sample extremes. J. R. Stat. Soc. Ser. B Stat. Methodol. 62 191–208.Mathematical Reviews (MathSciNet): MR1747404
Zentralblatt MATH: 0942.62058
Digital Object Identifier: doi:10.1111/1467-9868.00228 - Davison, A. C. and Smith, R. L. (1990). Models for exceedances over high thresholds. J. Roy. Statist. Soc. Ser. B 52 393–442.Mathematical Reviews (MathSciNet): MR1086795
- de Haan, L. and Ferreira, A. (2006). Extreme Value Theory: An Introduction. Springer, New York.Mathematical Reviews (MathSciNet): MR2234156
- de Haan, L. and Pereira, T. T. (2006). Spatial extremes: Models for the stationary case. Ann. Statist. 34 146–168.Mathematical Reviews (MathSciNet): MR2275238
Zentralblatt MATH: 1104.60021
Digital Object Identifier: doi:10.1214/009053605000000886
Project Euclid: euclid.aos/1146576259 - de Haan, L. and Zhou, C. (2008). On extreme value analysis of a spatial process. REVSTAT 6 71–81.Mathematical Reviews (MathSciNet): MR2386300
- Demarta, S. and McNeil, A. J. (2005). The $t$ copula and related copulas. International Statistical Review 73 111–129.
- Diggle, P. J. and Ribeiro, P. J. Jr. (2007). Model-based Geostatistics. Springer, New York.Mathematical Reviews (MathSciNet): MR2293378
- Diggle, P. J., Tawn, J. A. and Moyeed, R. A. (1998). Model-based geostatistics. J. Roy. Statist. Soc. Ser. C 47 299–350. With discussion and a reply by the authors.
- Einmahl, J. H. J. and Segers, J. (2009). Maximum empirical likelihood estimation of the spectral measure of an extreme-value distribution. Ann. Statist. 37 2953–2989.Mathematical Reviews (MathSciNet): MR2541452
Digital Object Identifier: doi:10.1214/08-AOS677
Project Euclid: euclid.aos/1247836674 - Embrechts, P., Klüppelberg, C. and Mikosch, T. (1997). Modelling Extremal Events: For Insurance and Finance. Applications of Mathematics (New York) 33. Springer, Berlin.Mathematical Reviews (MathSciNet): MR1458613
- Fawcett, L. and Walshaw, D. (2006). A hierarchical model for extreme wind speeds. J. Roy. Statist. Soc. Ser. C 55 631–646.Mathematical Reviews (MathSciNet): MR2291409
Zentralblatt MATH: 1109.62115
Digital Object Identifier: doi:10.1111/j.1467-9876.2006.00557.x - Finkenstädt, B. and Rootzén, H. (2004). Extreme Values in Finance, Telecommunications, and the Environment. Chapman & Hall/CRC, New York.
- Fougères, A. L. (2004). Multivariate extremes. In Extreme Values in Finance, Telecommunications, and the Environment (B. Finkenstädt and H. Rootzén, eds.) 373–388. Chapman & Hall/CRC, New York.
- Gaetan, C. and Grigoletto, M. (2007). A hierarchical model for the analysis of spatial rainfall extremes. J. Agric. Biol. Environ. Stat. 12 434–449.Mathematical Reviews (MathSciNet): MR2405533
Digital Object Identifier: doi:10.1198/108571107X250193 - Galambos, J. (1987). The Asymptotic Theory of Extreme Order Statistics, 2nd ed. Krieger, Melbourne, FL.Mathematical Reviews (MathSciNet): MR936631
- Genton, M. G., Ma, Y. and Sang, H. (2011). On the likelihood function of Gaussian max-stable processes. Biometrika 98 481–488.Mathematical Reviews (MathSciNet): MR2806443
Zentralblatt MATH: 1215.62089
Digital Object Identifier: doi:10.1093/biomet/asr020 - Gholamrezaee, M. M. (2010). Geostatistics of extremes: A composite likelihood approach. Ph.D. thesis, Ecole Polytechnique Fédérale de Lausanne.
- Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. (1996). Markov Chain Monte Carlo in Practice. Chapman & Hall, London.
- Gneiting, T., Sasvári, Z. and Schlather, M. (2001). Analogies and correspondences between variograms and covariance functions. Adv. in Appl. Probab. 33 617–630.Mathematical Reviews (MathSciNet): MR1860092
Zentralblatt MATH: 0987.86004
Digital Object Identifier: doi:10.1239/aap/1005091356
Project Euclid: euclid.aap/1005091356 - Hall, P. and Tajvidi, N. (2000). Nonparametric analysis of temporal trend when fitting parametric models to extreme-value data. Statist. Sci. 15 153–167.Mathematical Reviews (MathSciNet): MR1788730
Digital Object Identifier: doi:10.1214/ss/1009212755
Project Euclid: euclid.ss/1009212755 - Heffernan, J. E. and Tawn, J. A. (2004). A conditional approach for multivariate extreme values. J. R. Stat. Soc. Ser. B Stat. Methodol. 66 497–546.Mathematical Reviews (MathSciNet): MR2088289
Zentralblatt MATH: 1046.62051
Digital Object Identifier: doi:10.1111/j.1467-9868.2004.02050.x - Huser, R. and Davison, A. C. (2012). Space-time modelling of extreme events. Unpublished manuscript.
- Hüsler, J. and Reiss, R.-D. (1989). Maxima of normal random vectors: Between independence and complete dependence. Statist. Probab. Lett. 7 283–286.Mathematical Reviews (MathSciNet): MR980699
- Joe, H. (1997). Multivariate Models and Dependence Concepts. Chapman & Hall, London.
- Kabluchko, Z., Schlather, M. and de Haan, L. (2009). Stationary max-stable fields associated to negative definite functions. Ann. Probab. 37 2042–2065.Mathematical Reviews (MathSciNet): MR2561440
Zentralblatt MATH: 1208.60051
Digital Object Identifier: doi:10.1214/09-AOP455
Project Euclid: euclid.aop/1253539863 - Kotz, S. and Nadarajah, S. (2000). Extreme Value Distributions: Theory and Applications. Imperial College Press, London.Mathematical Reviews (MathSciNet): MR1892574
- Laurini, F. and Pauli, F. (2009). Smoothing sample extremes: The mixed model approach. Comput. Statist. Data Anal. 53 3842–3854.Mathematical Reviews (MathSciNet): MR2749928
- Leadbetter, M. R., Lindgren, G. and Rootzén, H. (1983). Extremes and Related Properties of Random Sequences and Processes. Springer, New York.
- Ledford, A. W. and Tawn, J. A. (1996). Statistics for near independence in multivariate extreme values. Biometrika 83 169–187.Mathematical Reviews (MathSciNet): MR1399163
Zentralblatt MATH: 0865.62040
Digital Object Identifier: doi:10.1093/biomet/83.1.169 - Ledford, A. W. and Tawn, J. A. (1997). Modelling dependence within joint tail regions. J. Roy. Statist. Soc. Ser. B 59 475–499.
- Lindsay, B. G. (1988). Composite likelihood methods. In Statistical Inference from Stochastic Processes (Ithaca, NY, 1987). Contemp. Math. 80 221–239. Amer. Math. Soc., Providence, RI.Mathematical Reviews (MathSciNet): MR999014
Zentralblatt MATH: 0672.62069
Digital Object Identifier: doi:10.1090/conm/080/999014 - Martins, E. and Stedinger, J. (2000). Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data. Water Resources Research 36 737–744.
- Mikosch, T. (2006). Copulas: Tales and facts. Extremes 9 3–62. With discussion.
- Naveau, P., Guillou, A., Cooley, D. and Diebolt, J. (2009). Modelling pairwise dependence of maxima in space. Biometrika 96 1–17.Mathematical Reviews (MathSciNet): MR2482131
Zentralblatt MATH: 1162.62045
Digital Object Identifier: doi:10.1093/biomet/asp001 - Nelsen, R. B. (2006). An Introduction to Copulas, 2nd ed. Springer, New York.Mathematical Reviews (MathSciNet): MR2197664
- Nikoloulopoulos, A. K., Joe, H. and Li, H. (2009). Extreme value properties of multivariate $t$ copulas. Extremes 12 129–148.Mathematical Reviews (MathSciNet): MR2515644
Digital Object Identifier: doi:10.1007/s10687-008-0072-4 - Oesting, M., Kabluchko, Z. and Schlather, M. (2012). Simulation of Brown–Resnick processes. Extremes 15 89–107.Mathematical Reviews (MathSciNet): MR2891311
Digital Object Identifier: doi:10.1007/s10687-011-0128-8 - Padoan, S. A., Ribatet, M. and Sisson, S. A. (2010). Likelihood-based inference for max-stable processes. J. Amer. Statist. Assoc. 105 263–277.Mathematical Reviews (MathSciNet): MR2757202
Digital Object Identifier: doi:10.1198/jasa.2009.tm08577 - Padoan, S. A. and Wand, M. P. (2008). Mixed model-based additive models for sample extremes. Statist. Probab. Lett. 78 2850–2858.Mathematical Reviews (MathSciNet): MR2516806
- Pauli, F. and Coles, S. (2001). Penalized likelihood inference in extreme value analyses. J. Appl. Stat. 28 547–560.Mathematical Reviews (MathSciNet): MR1855732
Zentralblatt MATH: 0991.62031
Digital Object Identifier: doi:10.1080/02664760120047889 - Pickands, J. III (1981). Multivariate extreme value distributions. In Proceedings of the 43rd Session of the International Statistical Institute, Vol. 2 (Buenos Aires, 1981). Bull. Inst. Internat. Statist. 49 859–878, 894–902.Mathematical Reviews (MathSciNet): MR820979
- Ramos, A. and Ledford, A. (2009). A new class of models for bivariate joint tails. J. R. Stat. Soc. Ser. B Stat. Methodol. 71 219–241.Mathematical Reviews (MathSciNet): MR2655531
Zentralblatt MATH: 1231.62093
Digital Object Identifier: doi:10.1111/j.1467-9868.2008.00684.x - Reich, B. J. and Shaby, B. A. (2011). A hierarchical Bayesian analysis of max-stable spatial processes. Unpublished manuscript.
- Resnick, S. I. (1987). Extreme Values, Regular Variation, and Point Processes. Springer, New York.Mathematical Reviews (MathSciNet): MR900810
- Resnick, S. I. (2007). Heavy-tail Phenomena: Probabilistic and Statistical Modeling. Springer, New York.
- Ribatet, M., Cooley, D. and Davison, A. C. (2012). Bayesian inference from composite likelihoods, with an application to spatial extremes. Statist. Sinica 22 813–845.
- Robert, C. P. and Casella, G. (2004). Monte Carlo Statistical Methods, 2nd ed. Springer, New York.Mathematical Reviews (MathSciNet): MR2080278
- Sang, H. and Gelfand, A. E. (2009). Hierarchical modeling for extreme values observed over space and time. Environ. Ecol. Stat. 16 407–426.Mathematical Reviews (MathSciNet): MR2749848
Digital Object Identifier: doi:10.1007/s10651-007-0078-0 - Sang, H. and Gelfand, A. E. (2010). Continuous spatial process models for spatial extreme values. J. Agric. Biol. Environ. Stat. 15 49–65.Mathematical Reviews (MathSciNet): MR2755384
Digital Object Identifier: doi:10.1007/s13253-009-0010-1 - Schabenberger, O. and Gotway, C. A. (2005). Statistical Methods for Spatial Data Analysis. Chapman & Hall/CRC, Boca Raton, FL.
- Schlather, M. (2002). Models for stationary max-stable random fields. Extremes 5 33–44.
- Schlather, M. and Tawn, J. A. (2003). A dependence measure for multivariate and spatial extreme values: Properties and inference. Biometrika 90 139–156.Mathematical Reviews (MathSciNet): MR1966556
Zentralblatt MATH: 1035.62045
Digital Object Identifier: doi:10.1093/biomet/90.1.139 - Smith, R. L. (1989). Extreme value analysis of environmental time series: An application to trend detection in ground-level ozone. Statist. Sci. 4 367–393.Mathematical Reviews (MathSciNet): MR1041763
Digital Object Identifier: doi:10.1214/ss/1177012400
Project Euclid: euclid.ss/1177012400 - Smith, R. L. (1990). Max-stable processes and spatial extremes. Unpublished manuscript.
- Smith, E. L. and Stephenson, A. G. (2009). An extended Gaussian max-stable process model for spatial extremes. J. Statist. Plann. Inference 139 1266–1275.Mathematical Reviews (MathSciNet): MR2485124
Zentralblatt MATH: 1153.62067
Digital Object Identifier: doi:10.1016/j.jspi.2008.08.003 - Stein, M. L. (1999). Interpolation of Spatial Data: Some Theory for Kriging. Springer, New York.Mathematical Reviews (MathSciNet): MR1697409
- Takeuchi, K. (1976). Distribution of informational statistics and a criterion of fitting. Suri-Kagaku 153 12–18 (in Japanese).
- Turkman, K. F., Turkman, M. A. A. and Pereira, J. M. (2010). Asymptotic models and inference for extremes of spatio-temporal data. Extremes 13 375–397.Mathematical Reviews (MathSciNet): MR2733939
Digital Object Identifier: doi:10.1007/s10687-009-0092-8 - Varin, C. (2008). On composite marginal likelihoods. AStA Adv. Stat. Anal. 92 1–28.Mathematical Reviews (MathSciNet): MR2414624
Digital Object Identifier: doi:10.1007/s10182-008-0060-7 - Varin, C. and Vidoni, P. (2005). A note on composite likelihood inference and model selection. Biometrika 92 519–528.Mathematical Reviews (MathSciNet): MR2202643
Zentralblatt MATH: 1183.62037
Digital Object Identifier: doi:10.1093/biomet/92.3.519 - Wackernagel, H. (2003). Multivariate Geostatistics: An Introduction with Applications, 3rd ed. Springer, New York.
- Wadsworth, J. L. and Tawn, J. A. (2012). Dependence modelling for spatial extremes. Biometrika 99. To appear.
- Zhang, H. (2004). Inconsistent estimation and asymptotically equal interpolations in model-based geostatistics. J. Amer. Statist. Assoc. 99 250–261.Mathematical Reviews (MathSciNet): MR2054303
Zentralblatt MATH: 1089.62538
Digital Object Identifier: doi:10.1198/016214504000000241
See also
- Discussion of: “Statistical Modeling of Spatial Extremes” by A. C. Davison, S. A. Padoan and M. Ribatet. Digital Object Identifier: doi:10.1214/12-STS376A
- Discussion of: “Statistical Modeling of Spatial Extremes” by A. C. Davison, S. A. Padoan and M. Ribatet. Digital Object Identifier: doi:10.1214/12-STS376B
- Discussion of: Nonparametric Inference for Max-Stable Dependence. Digital Object Identifier: doi:10.1214/12-STS376C
- Discussion of: “Statistical Modeling of Spatial Extremes” by A. C. Davison, S. A. Padoan and M. Ribatet. Digital Object Identifier: doi:10.1214/12-STS376D
- Rejoinder: Statistical Modeling of Spatial Extremes. Digital Object Identifier: doi:10.1214/12-STS376REJ

- You have access to this content.
- You have partial access to this content.
- You do not have access to this content.
More like this
- Bayesian inference for multivariate extreme value distributions
Dombry, Clément, Engelke, Sebastian, and Oesting, Marco, Electronic Journal of Statistics, 2017 - Conditional sampling for spectrally discrete max-stable random fields
Wang, Yizao and Stoev, Stilian A., Advances in Applied Probability, 2011 - Spatial–temporal mesoscale modeling of rainfall
intensity using gage and radar data
Fuentes, Montserrat, Reich, Brian, and Lee, Gyuwon, Annals of Applied Statistics, 2008
- Bayesian inference for multivariate extreme value distributions
Dombry, Clément, Engelke, Sebastian, and Oesting, Marco, Electronic Journal of Statistics, 2017 - Conditional sampling for spectrally discrete max-stable random fields
Wang, Yizao and Stoev, Stilian A., Advances in Applied Probability, 2011 - Spatial–temporal mesoscale modeling of rainfall
intensity using gage and radar data
Fuentes, Montserrat, Reich, Brian, and Lee, Gyuwon, Annals of Applied Statistics, 2008 - Modelling categorized levels of precipitation
Velozo, Patrícia Lusié, Alves, Mariane B., and Schmidt, Alexandra M., Brazilian Journal of Probability and Statistics, 2014 - A hierarchical max-stable spatial model for
extreme precipitation
Reich, Brian J. and Shaby, Benjamin A., Annals of Applied Statistics, 2012 - Population counts along elliptical habitat contours: Hierarchical modeling using Poisson-lognormal mixtures with nonstationary spatial structure
Schmidt, Alexandra M., Rodríguez, Marco A., and Capistrano, Estelina S., Annals of Applied Statistics, 2015 - Comment on Article by Page and Quintana
Gaetan, Carlo, Padoan, Simone A., and Prünster, Igor, Bayesian Analysis, 2016 - A Poisson Cluster Stochastic Rainfall Generator That Accounts for the Interannual Variability of Rainfall Statistics: Validation at Various Geographic Locations across the United States
Kim, Dongkyun, Kim, Jongho, and Cho, Yong-Sik, Journal of Applied Mathematics, 2014 - Statistical Assessment of Numerical Models
Fuentes, Montserrat, Guttorp, Peter, and Challenor, Peter, International Statistical Review, 2003 - Bayesian inference for the Brown–Resnick process, with an application to extreme low temperatures
Thibaud, Emeric, Aalto, Juha, Cooley, Daniel S., Davison, Anthony C., and Heikkinen, Juha, Annals of Applied Statistics, 2016
