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On Discrete Gamma Distribution
Communications in Statistics - Theory and Methods, 2015A new discrete counterpart of gamma distribution for modelling discrete life data is defined based on similar mathematical form and properties of the continuous version. The main statistical and reliability properties of this distribution are derived and it is shown that this model can deal with both over and under-dispersed data.
Najla S. Alhazzani +1 more
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1980
The general form of the gamma distribution is $$ f(x\left| {\alpha ,\beta } \right.) = \beta ^\alpha x^{\alpha - 1} e^{ - \beta x} /\Gamma (x){\text{ }}\:x \geqslant 0 $$ The parameter α is a shape parameter and s a scale parameter.
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The general form of the gamma distribution is $$ f(x\left| {\alpha ,\beta } \right.) = \beta ^\alpha x^{\alpha - 1} e^{ - \beta x} /\Gamma (x){\text{ }}\:x \geqslant 0 $$ The parameter α is a shape parameter and s a scale parameter.
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Analysis of progressive type-II censored gamma distribution
Computational statistics (Zeitschrift), 2022S. Dey, A. Elshahhat, M. Nassar
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, 2014
The exponentiated gamma (EG) distribution is one of the important families of distributions in lifetime tests. In this paper, a new generalized version of this distribution which is called Transmuted exponentiated gamma (TEG) distribution is introduced ...
M. Hussian
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The exponentiated gamma (EG) distribution is one of the important families of distributions in lifetime tests. In this paper, a new generalized version of this distribution which is called Transmuted exponentiated gamma (TEG) distribution is introduced ...
M. Hussian
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Properties of estimators for the gamma distribution
Communications in Statistics - Simulation and Computation, 1982Accurate moments of maximum likelihood and moment estimators for the scale and shape parameters of a two parameter gamma density are given, the former being tabulated over a segment of the parameter space. In addition, joint acceptance regions are given for a particular case.
L. R. Shenton, K. O. Bowman
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A Lagrangian Gamma Distribution
1975A new generalization of the gamma distribution is developed. This generalization, called the Lagrangian gamma, is the distribution of the time between occurrences of a generalized Poisson process. Moments of the distribution are derived, plots of the density are reproduced, and methods for parameter estimation are discussed.
D. L. Nelson, P. C. Consul
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Earth and Environmental Sciences Library, 2021
Jose A. Raynal Villaseñor
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Jose A. Raynal Villaseñor
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Statistics, 2008
Three new generalizations of the standard gamma distribution introduced by the author are reviewed. Various properties are derived for each distribution, including its hazard rate function and moments. An application is illustrated to drought data.
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Three new generalizations of the standard gamma distribution introduced by the author are reviewed. Various properties are derived for each distribution, including its hazard rate function and moments. An application is illustrated to drought data.
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On a multivariate gamma distribution [PDF]
Statistics & Probability Letters, 2008 Abstract A multivariate probability model possessing a dependence structure that is reflected in its variance–covariance structure and gamma distributed univariate margins is introduced and studied. In particular, the higher order moments and cumulants, Chebyshev-type inequalities and multivariate probability density functions are derived.
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Mixtures of Gamma Distributions With Applications
Journal of Computational and Graphical Statistics, 2001This article proposes a Bayesian density estimation method based upon mixtures of gamma distributions. It considers both the cases of known mixture size, using a Gibbs sampling scheme with a Metropolis step, and unknown mixture size, using a reversible jump technique that allows us to move from one mixture size to another.
Wiper Michael Peter +2 more
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