Semiparametric estimation for space-time max-stable processes: an F-madogram-based approach

A. Abu-Awwad (), V. Maume-Deschamps () and P. Ribereau ()
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A. Abu-Awwad: Faculty of Graduate Studies
V. Maume-Deschamps: Université de Lyon, Université Claude Bernard Lyon 1, Institute Camille Jordan ICJ UMR 5208 CNRS
P. Ribereau: Université de Lyon, Université Claude Bernard Lyon 1, Institute Camille Jordan ICJ UMR 5208 CNRS

Statistical Inference for Stochastic Processes, 2021, vol. 24, issue 2, No 1, 276 pages

Abstract: Abstract Max-stable processes have been expanded to quantify extremal dependence in spatiotemporal data. Due to the interaction between space and time, spatiotemporal data are often complex to analyze. So, characterizing these dependencies is one of the crucial challenges in this field of statistics. This paper suggests a semiparametric inference methodology based on the spatiotemporal F-madogram for estimating the parameters of a space-time max-stable process using gridded data. The performance of the method is investigated through various simulation studies. Finally, we apply our inferential procedure to quantify the extremal behavior of radar rainfall data in a region in the State of Florida.

Keywords: Extreme value theory; F-madogram; Max-stable process; Semiparametric estimation; Space-time process (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11203-020-09232-2

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