Minimax estimation of the parameters of the multivariate hypergeometric distribution
S. Trybuła (1985)
Applicationes Mathematicae
Similarity:
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
Displaying similar documents to “Minimax estimation of the parameters of the multivariate hypergeometric and multinomial distributions”
Minimax estimation of the parameters of the multivariate hypergeometric distribution
MLE for the γ-order Generalized Normal Distribution
Christos P. Kitsos, Vassilios G. Vassiliadis, Thomas L. Toulias (2014)
Discussiones Mathematicae Probability and Statistics
Similarity:
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...
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
Similarity:
Gamma-Minimax Estimation of a Multivariate Normal Mean.
J. Lehn, L. Chen, J. Eichenauer-Herrmann (1990)
Metrika
Similarity:
Parameter Estimation for a Bivariate Lifetime Distribution in Reliability with Multivariate Extensions.
G. Heinrich, U. Jensen (1995)
Metrika
Similarity:
Simultaneous minimax estimation of parameters of multinomial distribution
Stanisław Trybuła (2002)
Applicationes Mathematicae
Similarity:
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.
Multivariate Gamma Distributions with One-Factorial Accompanying Correlation Matrices and Applications to the Distribution of the Multivariate Range.
T. Royen (1991)
Metrika
Similarity:
Minimum Variance Unbiased Estimation of Multivariate Modified Power Series Distribution.
S.R. Patel (1978)
Metrika
Similarity:
Construction of multivariate distributions: a review of some recent results.
José María Sarabia, Emilio Gómez-Déniz (2008)
SORT
Similarity:
The construction of multivariate distributions is an active field of research in theoretical and applied statistics. In this paper some recent developments in this field are reviewed. Specifically, we study and review the following set of methods: (a) Construction of multivariate distributions based on order statistics, (b) Methods based on mixtures, (c) Conditionally specified distributions, (d) Multivariate skew distributions, (e) Distributions based on the method of the variables...
Multivariate Survival Functions Characterized by Constant Product of Mean Remaining Lives and Hazard Rates.
Chunsheng Ma (1996)
Metrika
Similarity:
On the multivariate skew-normal distribution and its scale mixtures.
Vernic, Raluca (2005)
Analele Ştiinţifice ale Universităţii “Ovidius" Constanţa. Seria: Matematică
Similarity:
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]
Similarity:
Characterization of the Multivariate Normal Distribution by Conditional Normal Distributions.
W. Fieger, W. Bischoff (1991)
Metrika
Similarity:
Minimax prediction of a sample distribution function
S. Trybuła (1978)
Applicationes Mathematicae
Similarity:
CMPH: a multivariate phase-type aggregate loss distribution
Jiandong Ren, Ricardas Zitikis (2017)
Dependence Modeling
Similarity:
We introduce a compound multivariate distribution designed for modeling insurance losses arising from different risk sources in insurance companies. The distribution is based on a discrete-time Markov Chain and generalizes the multivariate compound negative binomial distribution, which is widely used for modeling insurance losses.We derive fundamental properties of the distribution and discuss computational aspects facilitating calculations of risk measures of the aggregate loss, as...