A Note on Second Order Conditions in Extreme Value Theory: Linking General and Heavy Tail Conditions
DOI:
https://doi.org/10.57805/revstat.v5i3.53Keywords:
extreme value index, regular variation, semi-parametric estimationAbstract
Second order conditions ruling the rate of convergence in any first order condition involving regular variation and assuring a unified extreme value limiting distribution function for the sequence of maximum values, linearly normalized, have appeared in several contexts whenever researchers are working either with a general tail, i.e., γ ∈ R, or with heavy tails, with an extreme value index γ > 0. In this paper we shall clarify the link between the second order parameters, say ρ and ρe that have appeared in the two above mentioned set-ups, i.e., for a general tail and for heavy tails, respectively. We illustrate the theory with some examples and, for heavy tails, we provide a link with a third order framework.
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Copyright (c) 2007 REVSTAT-Statistical Journal
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