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Asymptotic comparison at optimal levels of reduced‐bias extreme value index estimators
Author(s) -
Caeiro Frederico,
Gomes M. Ivette
Publication year - 2011
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.2011.00495.x
Subject(s) - estimator , mathematics , index (typography) , extreme value theory , statistics , parametric statistics , set (abstract data type) , class (philosophy) , value (mathematics) , econometrics , generalized extreme value distribution , computer science , artificial intelligence , world wide web , programming language
In this article we are interested in the asymptotic comparison, at optimal levels, of a set of semi‐parametric reduced‐bias extreme value (EV) index estimators, valid for a wide class of heavy‐tailed models, underlying the available data. Again, as in the classical case, there is not any estimator that can always dominate the alternatives, but interesting clear‐cut patterns are found. Consequently, and in practice, a suitable choice of a set of EV index estimators will jointly enable us to better estimate the EV index γ , the primary parameter of extreme events.