A class of unbiased location invariant Hill-type estimators for heavy tailed distributions

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dc.creatorLi, Jiaona
dc.creatorPeng, Zuoxiang
dc.creatorNadarajah, Saralees
dc.date2008-09-23
dc.date.accessioned2026-07-07T10:04:38Z
dc.date.available2026-07-07T10:04:38Z
dc.descriptionBased on the methods provided in Caeiro and Gomes (2002) and Fraga Alves (2001), a new class of location invariant Hill-type estimators is derived in this paper. Its asymptotic distributional representation and asymptotic normality are presented, and the optimal choice of sample fraction by Mean Squared Error is also discussed for some special cases. Finally comparison studies are provided for some familiar models by Monte Carlo simulations.
dc.descriptionPublished in at http://dx.doi.org/10.1214/08-EJS276 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0809.3869
dc.identifierhttp://arxiv.org/abs/0809.3869
dc.identifierElectronic Journal of Statistics 2008, Vol. 2, 829-847
dc.identifierdoi:10.1214/08-EJS276
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/169751
dc.subjectStatistics Theory
dc.subject62G32 (Primary) 65C05 (Secondary)
dc.titleA class of unbiased location invariant Hill-type estimators for heavy tailed distributions
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