On the convergence with probability one for a sequence of empirical Bayes estimators
J. Kogut (1980)
Applicationes Mathematicae
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Displaying similar documents to “Uniform asymptotic normality for the Bernoulli scheme”
On the convergence with probability one for a sequence of empirical Bayes estimators
Asymptotic normality and efficiency of variance components estimators with high breakdown points
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...
A uniform central limit theorem for dependent variables
Konrad Furmańczyk (2009)
Applicationes Mathematicae
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Niemiro and Zieliński (2007) have recently obtained uniform asymptotic normality for the Bernoulli scheme. This paper concerns a similar problem. We show the uniform central limit theorem for a sequence of stationary random variables.
Sequential confidence intervals based on robust estimators
Jana Jurečková (1985)
Banach Center Publications
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First and second order asymptotic efficiencies of estimators
C. Radhakrishna Rao (1962)
Annales scientifiques de l'Université de Clermont. Mathématiques
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Generalized Bayesian-type estimators. Robust and sensitivity analysis
Jan Hanousek (1994)
Kybernetika
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Risk-Efficient Nonparametric Sequential Estimators.
W. Goldmann (1987)
Metrika
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On estimation of reliability in the exponential case
Jan Hurt (1976)
Aplikace matematiky
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Adaptive procedures
Marie Hušková (1983)
Acta Universitatis Carolinae. Mathematica et Physica
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Kernel estimators and the Dvoretzky-Kiefer-Wolfowitz inequality
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.