Generalized jackknife semi-parametric estimators of the tail index.
Gomes, M.Ivette, Martins, M.João, Neves, Manuela (2002)
Portugaliae Mathematica. Nova Série
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Gomes, M.Ivette, Martins, M.João, Neves, Manuela (2002)
Portugaliae Mathematica. Nova Série
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Fraga Alves, M.I., Gomes, M.Ivette, de Haan, Laurens (2003)
Portugaliae Mathematica. Nova Série
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M. Ivette Gomes, Lígia Henriques-Rodrigues (2010)
Discussiones Mathematicae Probability and Statistics
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In this article, we begin with an asymptotic comparison at optimal levels of the so-called "maximum likelihood" (ML) extreme value index estimator, based on the excesses over a high random threshold, denoted PORT-ML, with PORT standing for peaks over random thresholds, with a similar ML estimator, denoted PORT-MP, with MP standing for modified-Pareto. The PORT-MP estimator is based on the same excesses, but with a trial of accommodation of bias on the Generalized Pareto model underlying...
Hakim Ouadjed, Tawfiq Fawzi Mami (2018)
Kybernetika
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In this paper, we propose two estimators for a heavy tailed MA(1) process. The first is a semi parametric estimator designed for MA(1) driven by positive-value stable variables innovations. We study its asymptotic normality and finite sample performance. We compare the behavior of this estimator in which we use the Hill estimator for the extreme index and the estimator in which we use the t-Hill in order to examine its robustness. The second estimator is for MA(1) driven by stable variables...
Álex Costa, Albert Satorra, Eva Ventura (2003)
SORT
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This paper compares five small area estimators. We use Monte Carlo simulation in the context of both artificial and real populations. In addition to the direct and indirect estimators, we consider the optimal composite estimator with population weights, and two composite estimators with estimated weights: one that assumes homogeneity of within area variance and squared bias and one that uses area-specific estimates of variance and squared bias. In the study with real population, we found...
J. Jurečková (1983)
Acta Universitatis Carolinae. Mathematica et Physica
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Nicholas T. Longford (2008)
SORT
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The one-way analysis of variance is a staple of elementary statistics courses. The hypothesis test of homogeneity of the means encourages the use of the selected-model based estimators which are usually assessed without any regard for the uncertainty about the outcome of the test. We expose the weaknesses of such estimators when the uncertainty is taken into account, as it should be, and propose synthetic estimators as an alternative.
Vassiliy G. Voinov, Mikhail S. Nikulin (1995)
Qüestiió
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Since 1956, a large number of papers have been devoted to Stein's technique of obtaining improved estimators of parameters, for several statistical models. We give a brief review of these papers, emphasizing those aspects which are interesting from the point of view of the theory of unbiased estimation.
HousiLA P. SINGH AND M. RUIZ ESPEJO (1999)
Revista de la Real Academia de Ciencias Exactas Físicas y Naturales
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Christine H. Müller (2000)
Discussiones Mathematicae Probability and Statistics
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For estimating the variance components of a one-way random effect model recently Uhlig (1995, 1997) and Lischer (1996) proposed non-iterative estimators with high breakdown points. These estimators base on the high breakdown point scale estimators of Rousseeuw and Croux (1992, 1993), which they called Q-estimators. In this paper the asymptotic normal distribution of the new variance components estimators is derived so that the asymptotic efficiency of these estimators can be compared...
Asunción Rubio, Jan Amos Visek (1991)
Trabajos de Estadística
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The paper studies the problem of selecting an estimator with (approximately) minimal asymptotic variance. For every fixed contamination level there is usually just one such estimator in the considered family. Using the first and the second derivative of the asymptotic variance with respect to the parameter which parametrizes the family of estimators the paper gives two examples of how to select the estimator and gives an approximation to a loss which we suffer when we use the estimator...
Ryszard Zieliński (2003)
Applicationes Mathematicae
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If θ ∈ Θ is an unknown real parameter of a given distribution, we are interested in constructing an exactly median-unbiased estimator θ̂ of θ, i.e. an estimator θ̂ such that a median Med(θ̂ ) of the estimator equals θ, uniformly over θ ∈ Θ. We shall consider the problem in the case of a fixed sample size n (nonasymptotic approach).