@article {1039.62041,
	title = {A class of asymptotically unbiased semi-parametric estimators of the tail index.},
	journal = {Test},
	volume = {11},
	number = {2},
	year = {2002},
	note = {<p>n/a</p>
},
	pages = {345-364},
	abstract = {<p>{Summary: We consider a class of consistent semi-parametric estimators of a positive tail index $\gamma$, parameterized by a tuning or control parameter $\alpha$. Such a control parameter enables us to have access, for any available sample, to an estimator of $\gamma$ with a null dominant component of asymptotic bias, and with a reasonably flat mean squared error pattern, as a function of $k$, the number of top order statistics considered. Moreover, we are able to achieve a high efficiency relative to the classical Hill estimator [{\i}t B. M. Hill}, Ann. Stat. 3, 1163{\textendash}1174 (1975; Zbl 0323.62033)], provided we may have access to a larger number of top order statistics than the number needed for optimal estimation through the Hill estimator.}</p>
},
	keywords = {finite sample behavior, robustness, simulations}, Student models, {extremes},
	doi = {10.1007/BF02595711},
	author = {Caeiro, Frederico and Gomes, M. Ivette}
}
