Statistical Inference for Max-Stable Processes by Conditioning on Extreme Events. (June 2014)

• Record Type: Journal Article
• Authors: Engelke, Sebastian; Malinowski, Alexander; Oesting, Marco; Schlather, Martin
• Is Part Of: Advances in applied probability. Volume 46:Number 2(2014)
• Page Start: 478
• Page End: 495
• Publication Date: 2014-06
• Subjects: Extreme value statistics, -- incremental representation, -- max-stable process, -- mixed moving maxima, -- peaks-over-threshold; 60G70, -- 62G32, -- 62E20; Probabilities -- Periodicals; Stochastic models -- Periodicals; Electronic journals; Periodicals; 519.2
• DOI: 10.1239/aap/1401369703
• Languages: English
• ISSNs: 0001-8678

Abstract

Abstract : In this paper we provide the basis for new methods of inference for max-stable processes ξ on general spaces that admit a certain incremental representation, which, in important cases, has a much simpler structure than the max-stable process itself. A corresponding peaks-over-threshold approach will incorporate all single events that are extreme in some sense and will therefore rely on a substantially larger amount of data in comparison to estimation procedures based on block maxima. Conditioning a process η in the max-domain of attraction of ξ on being extremal, several convergence results for the increments of η are proved. In a similar way, the shape functions of mixed moving maxima (M3) processes can be extracted from suitably conditioned single events η. Connecting the two approaches, transformation formulae for processes that admit both an incremental and an M3 representation are identified.