Two problems of minimax estimation
S. Trybuła (1974)
Applicationes Mathematicae
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Displaying similar documents to “Simultaneous minimax estimation of parameters of multinomial distribution”
Two problems of minimax estimation
A probabilistic approach to the estimation of the distribution of the target location point.
Tabatabai, M.A. (1995)
Southwest Journal of Pure and Applied Mathematics [electronic only]
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Minimax prediction of a sample distribution function
S. Trybuła (1978)
Applicationes Mathematicae
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Estimation of P{Y
J. Bartoszewicz (1977)
Applicationes Mathematicae
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Numerical methods for linear minimax estimation
Norbert Gaffke, Berthold Heiligers (2000)
Discussiones Mathematicae Probability and Statistics
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We discuss two numerical approaches to linear minimax estimation in linear models under ellipsoidal parameter restrictions. The first attacks the problem directly, by minimizing the maximum risk among the estimators. The second method is based on the duality between minimax and Bayes estimation, and aims at finding a least favorable prior distribution.
Fixed precision estimation of a normal mean
Beniamin Goldys (1985)
Banach Center Publications
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On One Estimation Problem
Jelena Bulatović, Alobodanka Janjić (1979)
Publications de l'Institut Mathématique
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Estimation of the generalized variance in a bivariate normal distribution from an incomplete sample
Wiktor Oktaba, Joanna Tarasińska (2001)
Applicationes Mathematicae
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The aim of the paper is estimation of the generalized variance of a bivariate normal distribution in the case of a sample with missing observations. The estimator based on all available observations is compared with the estimator based only on complete pairs of observations.
Minimax estimation of a cumulative distribution function for a special loss function
S Trybuła (1991)
Applicationes Mathematicae
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Minimax estimation of a class of functions of the scale parameter in the gamma and other distributions in the case of truncated parameter space
M. Kałuszka (1988)
Applicationes Mathematicae
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Estimating a discrete distribution via histogram selection
Nathalie Akakpo (2011)
ESAIM: Probability and Statistics
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Our aim is to estimate the joint distribution of a finite sequence of independent categorical variables. We consider the collection of partitions into dyadic intervals and the associated histograms, and we select from the data the best histogram by minimizing a penalized least-squares criterion. The choice of the collection of partitions is inspired from approximation results due to DeVore and Yu. Our estimator satisfies a nonasymptotic oracle-type inequality and adaptivity properties...
Set estimation: Another bridge between statistics and geometry.
A. Cuevas (2009)
Boletín de Estadística e Investigación Operativa. BEIO
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A sufficient condition for admissibility in linear estimation
Czesław Stępniak (1988)
Aplikace matematiky
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It was recently shown that all estimators which are locally best in the relative interior of the parameter set, together with their limits constitute a complete class in linear estimation, both unbiased and biased. However, not all these limits are admissible. A sufficient condition for admissibility of a limit was given by the author (1986) for the case of unbiased estimation in a linear model with the natural parameter space. This paper extends this result to the general linear model...
Estimation under sampling from a finite population
S. Trybuła, M. Wilczyński (1985)
Applicationes Mathematicae
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Estimating a discrete distribution histogram selection
Nathalie Akakpo (2011)
ESAIM: Probability and Statistics
Similarity:
Our aim is to estimate the joint distribution of a finite sequence of independent categorical variables. We consider the collection of partitions into dyadic intervals and the associated histograms, and we select from the data the best histogram by minimizing a penalized least-squares criterion. The choice of the collection of partitions is inspired from approximation results due to DeVore and Yu. Our estimator satisfies a nonasymptotic oracle-type inequality and adaptivity properties...