Minimax estimation of the parameters of the multivariate hypergeometric and multinomial distributions
M. Rutkowska (1977)
Applicationes Mathematicae
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Displaying similar documents to “Minimax estimation of the parameters of the multivariate hypergeometric distribution”
Minimax estimation of the parameters of the multivariate hypergeometric and multinomial distributions
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S.R. Patel (1978)
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Simultaneous minimax estimation of parameters of multinomial distribution
Stanisław Trybuła (2002)
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The problem of minimax estimation of parameters of multinomial distribution is considered for a loss function being the sum of the losses of the statisticians taking part in the estimation process.
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The introduced three parameter (position μ, scale ∑ and shape γ) multivariate generalized Normal distribution (γ-GND) is based on a strong theoretical background and emerged from Logarithmic Sobolev Inequalities. It includes a number of well known distributions such as the multivariate Uniform, Normal, Laplace and the degenerated Dirac distributions. In this paper, the cumulative distribution, the truncated distribution and the hazard rate of the γ-GND are presented. In addition, the...
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S. Trybuła, M. Wilczyński (1985)
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Tabatabai, M.A. (1995)
Southwest Journal of Pure and Applied Mathematics [electronic only]
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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)
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G. Heinrich, U. Jensen (1995)
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Multivariate Survival Functions Characterized by Constant Product of Mean Remaining Lives and Hazard Rates.
Chunsheng Ma (1996)
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S.M. Shah, S.R. Patel (1978)
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Minimax prediction of a sample distribution function
S. Trybuła (1978)
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Minimax Estimation for the Bounded Mean of a Bivariate Normal Distribution.
Wolfgang Bischoff, Werner Fieger, Sabine Ochtrop (1995)
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Minimax prediction under random sample size
Alicja Jokiel-Rokita (2002)
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A class of minimax predictors of random variables with multinomial or multivariate hypergeometric distribution is determined in the case when the sample size is assumed to be a random variable with an unknown distribution. It is also proved that the usual predictors, which are minimax when the sample size is fixed, are not minimax, but they remain admissible when the sample size is an ancillary statistic with unknown distribution.
Minimum Variance Unbiased Estimation of the Parameters of the Pareto Distribution.
M.A. Baxter (1980)
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