On Classes of Estimators of the Variance Function of a Linear Estimator.
S.G. Prabhu Ajgaonkar (1972)
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S.G. Prabhu Ajgaonkar (1972)
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R.D. Reiss (1978)
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W. jr. Eberl (1986)
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T. Kubokawa (1989)
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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.
J. Eichenauer-Herrmann, K. Ickstadt ([unknown])
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Byung Hwee Kim (1994)
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S.G. Prabhu-Ajgaonkar (1984)
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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.
S.G. Prabhu Ajgaonkar (1970)
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H.S. Konijn (1981)
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LanXiang Chen, J. Eichenauer-Herrmann, J. Lehn (1988)
Applicationes Mathematicae
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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...
HousiLA P. SINGH AND M. RUIZ ESPEJO (1999)
Revista de la Real Academia de Ciencias Exactas Físicas y Naturales
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