Risk-Efficient Nonparametric Sequential Estimators.
W. Goldmann (1987)
Metrika
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W. Goldmann (1987)
Metrika
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Jaromír Antoch, Marie Husková (2000)
Discussiones Mathematicae Probability and Statistics
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The purpose of this paper is to study Bayesian like R- and M-estimators of change point(s). These estimators have smaller variance than the related argmax type estimators. Confidence intervals for the change point based on the exchangeability arguments are constructed. Finally, theoretical results are illustrated on the real data set.
M. Hušková (1985)
Banach Center Publications
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Ryszard Zieliński (2007)
Applicationes Mathematicae
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It turns out that for standard kernel estimators no inequality like that of Dvoretzky-Kiefer-Wolfowitz can be constructed, and as a result it is impossible to answer the question of how many observations are needed to guarantee a prescribed level of accuracy of the estimator. A remedy is to adapt the bandwidth to the sample at hand.
M. Wilczyński (1985)
Applicationes Mathematicae
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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...
Erik Meijer, Ab Mooijart (1994)
Qüestiió
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Structural models are usually estimated using only second order moments (covariances or correlations). When variables are nor multivariate normally distributed, however, methods that also fit higher order moments, such as skewnesses, are theoretically asymptotically preferable. This article reports result from a Monte Carlo simulation study in which estimators that fit both second-order moments and third-order moments are compared with estimators that fit only second-order moments. ...
Irena Wistuba (1983)
Applicationes Mathematicae
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J. Bartoszewicz (1983)
Applicationes Mathematicae
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I. Malinowska, P. Pawlas, D. Szynal (2005)
Applicationes Mathematicae
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The minimum variance linear unbiased estimators (MVLUE), the best linear invariant estimators (BLIE) and the maximum likelihood estimators (MLE) based on m selected kth record values are presented for the parameters of the Gumbel and Burr distributions.
LanXiang Chen, J. Eichenauer-Herrmann, J. Lehn (1988)
Applicationes Mathematicae
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HousiLA P. SINGH AND M. RUIZ ESPEJO (1999)
Revista de la Real Academia de Ciencias Exactas Físicas y Naturales
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J. Kleffe (1979)
Applicationes Mathematicae
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W. Fieger, W. Bischoff ([unknown])
Metrika
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R.D. Reiss (1978)
Metrika
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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...
R. Zieliński, W. Zieliński (1984)
Applicationes Mathematicae
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