Geostatistics of extremes
We describe a prototype approach to flexible modelling for maxima observed at sites in a spatial domain, based on fitting of max-stable processes derived from underlying Gaussian random fields. The models we propose have generalised extreme-value marginal distributions throughout the spatial domain, consistent with statistical theory for maxima in simpler cases, and can incorporate both geostatistical correlation functions and random set components. Parameter estimation and fitting are performed through composite likelihood inference applied to observations from pairs of sites, with occurrence times of maxima taken into account if desired, and competing models are compared using appropriate information criteria. Diagnostics for lack of model fit are based on maxima from groups of sites. The approach is illustrated using annual maximum temperatures in Switzerland, with risk analysis proposed using simulation from the fitted max-stable model. Drawbacks and possible developments of the approach are discussed.
Keywords: Composite marginal likelihood; Extremal coefficient; Gaussian process; Generalized extreme-value distribution; Max-stable process; Spatial statistics; Statistics of extremes; Temperature data.
Record created on 2012-01-03, modified on 2016-08-09