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

Autor: Abu-Awwad, Abdul-Fattah, Maume-Deschamps, Véronique, Ribereau, Pierre
Přispěvatelé: Institut Camille Jordan [Villeurbanne] (ICJ), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Probabilités, statistique, physique mathématique (PSPM), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Statistical Inference for Stochastic Processes
Statistical Inference for Stochastic Processes, Springer Verlag, 2021
ISSN: 1387-0874
1572-9311
Popis: Max-stable processes have been expanded to quantify extremal dependence in spatio-temporal data. Due to the interaction between space and time, spatio-temporal 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 spatio-temporal 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.
Comment: arXiv admin note: text overlap with arXiv:1507.07750 by other authors
Databáze: OpenAIRE