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Parametric Estimation Procedures in Multivariate Generalized Pareto Models
Author(s) -
MICHEL RENÉ
Publication year - 2009
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2008.00619.x
Subject(s) - multivariate statistics , mathematics , estimator , generalized pareto distribution , parametric statistics , pareto principle , multivariate normal distribution , parametric model , estimation theory , statistics , extreme value theory
. Modelling the tails of a multivariate distribution can be reasonably done by multivariate generalized Pareto distributions (GPDs). We present several methods of parametric estimation in these models, which use decompositions of the corresponding random vectors with the help of different versions of Pickands coordinates. The estimators are compared to each other with simulated data sets. To show the practical value of the methods, they are applied to a real hydrological data set.