A flexible dependence model for spatial extremes

Autor: Carlo Gaetan, Jean-Noël Bacro, Gwaldys Toulemonde
Přispěvatelé: Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Scienze Ambientali, Informatica e Statistica [Venezia] (DAIS), University of Ca’ Foscari [Venice, Italy]
Rok vydání: 2016
Předmět:
Spatial extremes
Asymptotic independence
Max-stable processes MAX-STABLE PROCESSES
INFERENCE
LIKELIHOOD
GEOSTATISTICS
MULTIVARIATE
STATISTICS
VALUES
SPACE
TIME
Statistics and Probability
Multivariate statistics
010504 meteorology & atmospheric sciences
Structure (category theory)
Spatial extremes
01 natural sciences
LIKELIHOOD
010104 statistics & probability
Asymptotic independence
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]
Econometrics
SPACE
Statistical physics
0101 mathematics
Spatial dependence
Extreme value theory
ComputingMilieux_MISCELLANEOUS
Independence (probability theory)
0105 earth and related environmental sciences
Mathematics
VALUES
Applied Mathematics
Max-stable processes MAX-STABLE PROCESSES
Extension (predicate logic)
STATISTICS
TIME
MULTIVARIATE
INFERENCE
Pairwise comparison
GEOSTATISTICS
Statistics
Probability and Uncertainty
Settore SECS-S/01 - Statistica
Maxima
Zdroj: Journal of Statistical Planning and Inference
Journal of Statistical Planning and Inference, Elsevier, 2016, 172, pp.36-52. ⟨10.1016/j.jspi.2015.12.002⟩
ISSN: 0378-3758
1873-1171
Popis: Max-stable processes play a fundamental role in modeling the spatial dependence of extremes because they appear as a natural extension of multivariate extreme value distributions. In practice, a well-known restrictive assumption when using max-stable processes comes from the fact that the observed extremal dependence is assumed to be related to a particular max-stable dependence structure. As a consequence, the latter is imposed to all events which are more extreme than those that have been observed. Such an assumption is inappropriate in the case of asymptotic independence. Following recent advances in the literature, we exploit a max-mixture model to suggest a general spatial model which ensures extremal dependence at small distances, possible independence at large distances and asymptotic independence at intermediate distances. Parametric inference is carried out using a pairwise composite likelihood approach. Finally we apply our modeling framework to analyze daily precipitations over the East of Australia, using block maxima over the observation period and exceedances over a large threshold.
Databáze: OpenAIRE
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