Results 251 to 260 of about 1,023,504 (281)
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Abelian and Tauberian Theorems on the Bias of the Hill Estimator
Scandinavian Journal of Statistics, 2002The bias of Hill's estimator for the positive extreme value index of a distribution is investigated in relation to the convergence rate in the regular variation property of the tail function of the common distribution of the sample and the corresponding tail quantile function.
Johan Segers
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Some results on the behaviour of hill's estimator
Journal of Statistical Computation and Simulation, 1999The maximum of a large number of random variables, suitably normalized, follows, under certain general conditions, the Generalized Extreme Value (GEV) distribution . Hill’s estimator provides an estimation for γ > 0 from a finite sample, requiring the largest m observations, out of n.
Maria Ivette Gomes
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On an improvement of Hill and some other estimators
Lithuanian Mathematical Journal, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Vygantas Paulauskas
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Hill's estimator under weak dependence
Communications in Statistics - Theory and Methods, 2017ABSTRACTIn this article, we investigate the asymptotic normality of the Hill's estimator of the tail index parameter, when the observations are weakly dependent in the sense of Doukhan and Louhichi (1999) and are drawn from a strictly linear process. We show that the previous result on Hill estimator obtained by Rootzen et al.
Karima Boualam, Youcef Berkoun
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International Statistical Review, 2015
SummaryWe propose two classes of semi‐parametric estimators for the tail index of a regular varying elliptical random vector. The first one is based on the distance between a tail probability contour and the observations outside this contour. We denote it as the class ofseparatingestimators. The second one is based on the norm of an arbitrary order. We
Dominicy, Yves +3 more
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SummaryWe propose two classes of semi‐parametric estimators for the tail index of a regular varying elliptical random vector. The first one is based on the distance between a tail probability contour and the observations outside this contour. We denote it as the class ofseparatingestimators. The second one is based on the norm of an arbitrary order. We
Dominicy, Yves +3 more
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A simple generalisation of the Hill estimator
Computational Statistics & Data Analysis, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Maria de Fátima Brilhante +2 more
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The Asymptotic Behavior of Hill’s Estimator
Theory of Probability & Its Applications, 1987See the review in Zbl 0611.62036.
Beirlant, Jan, Teugels, J. K.
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SSRN Electronic Journal, 2013
We propose two classes of semi-parametric estimators for the tail index of a regular varying elliptical random vector. The first one is based on the distance between a tail probability contour and the observations outside this contour. We denote it as the class of separating estimators. The second one is based on the norm of an arbitrary order.
Dominicy, Yves +2 more
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We propose two classes of semi-parametric estimators for the tail index of a regular varying elliptical random vector. The first one is based on the distance between a tail probability contour and the observations outside this contour. We denote it as the class of separating estimators. The second one is based on the norm of an arbitrary order.
Dominicy, Yves +2 more
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On the asymptotic normality of Hill's estimator
Mathematical Proceedings of the Cambridge Philosophical Society, 1995LetX,X1,X2, …, be independent random variables with a common distribution functionF(x) =P{X≤x},x∈ℝ, and for eachn∈ℕ, letX1,n≤ … ≤Xn, ndenote the order statistics pertaining to the sampleX1, …,Xn. We assume that 1–F(x) =x−1/cl(x), 0 <x< ∞, wherelis some function slowly varying at infinity andc> 0 is any fixed number.
Csörgö, Sándor, Viharos, László
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Flexible multivariate Hill estimators
Journal of Econometrics, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dominicy, Yves +4 more
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