Space-time modelling of extreme events

Abstract

Max-stable processes are the natural analogues of the generalized extreme-value distribution for the modelling of extreme events in space and time. Under suitable conditions, these processes are asymptotically justified models for maxima of independent replications of random fields, and they are also suitable for the modelling of joint individual extreme measurements over high thresholds. This paper extends a model of Schlather (2001) to the space-time framework, and shows how a pairwise censored likelihood can be used for consistent estimation under mild mixing conditions. Estimator efficiency is also assessed and the choice of pairs to be included in the pairwise likelihood is discussed based on computations for simple time series models. The ideas are illustrated by an application to hourly precipitation data over Switzerland.

References in corpus (8)

• Statistical Modeling of Spatial Extremes
• Stationary max-stable fields associated to negative definite functions
• Spatial modeling of extreme snow depth
• A hierarchical max-stable spatial model for extreme precipitation
• On spatial extremes: with application to a rainfall problem
• Extremal t processes: Elliptical domain of attraction and a spectral representation
• ML, PL, QL in Markov chain models
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