The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform
A. Podraza-karakulska - One of the best experts on this subject based on the ideXlab platform.
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On estimation of the shape parameter of the Gamma Distribution
Statistics & Probability Letters, 2008Co-Authors: Alexander Zaigraev, A. Podraza-karakulskaAbstract:The problem of estimation of an unknown shape parameter under the sample drawn from the Gamma Distribution, where the scale parameter is also unknown, is considered. A new estimator, called the maximum likelihood scale invariant estimator, is proposed. It is established that both the bias and the variance of this estimator are less than that of the usual maximum likelihood estimator. A property of the psi function is also obtained.
Augustine C. M. Wong - One of the best experts on this subject based on the ideXlab platform.
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A note on inference for the mean parameter of the Gamma Distribution
Statistics & Probability Letters, 1993Co-Authors: Augustine C. M. WongAbstract:Abstract The two parameter Gamma Distribution with mean μ and shape τ is widely used in reliability and life data analysis. Unlike the normal Distribution, which also has two parameters describing the location and the scale, inference for the mean parameter of the Gamma Distribution is much more complicated (Jensen, 1986) and consequently less well developed. In this paper, a method of averaging is proposed to obtain confidence intervals for the mean parameter of the Gamma Distribution at an arbitrary level of significance. Numerical examples showed that this method is not only simple but also very accurate.
Norou Diawara - One of the best experts on this subject based on the ideXlab platform.
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A Multivariate Gamma Distribution and its Characterizations
American Journal of Mathematical and Management Sciences, 2007Co-Authors: Mark Carpenter, Norou DiawaraAbstract:SYNOPTIC ABSTRACTIn this paper, we proffer a new multivariate Gamma Distribution with potential applications in survival and reliability modeling. The multivariate Distribution is not necessarily restricted to those with Gamma marginal Distributions. We provide and characterize a generalized location scale family of multivariate Gamma Distributions. This family possesses three-parameter Gamma marginals (in most cases) and it contains absolutely continuous classes, as well as, the Marshall Olkin type of Distributions with a positive probability mass on a set of measure zero. Maximum likelihood estimators are developed in the bivariate case.
Jozef Dopke - One of the best experts on this subject based on the ideXlab platform.
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Estimation of Parameters in Gamma Distribution
International Journal of Quality & Reliability Management, 1994Co-Authors: Jozef DopkeAbstract:Consideration of the reliability of products can be frequently described by Gamma Distribution. The Johnson estimation method for any data and simplified maximum likelihood estimation method for complete samples can be used to assess the parameters of this Distribution. Describes the application of IBM PC programs to determine the parameters of Gamma Distribution according to this method.
Saralees Nadarajah - One of the best experts on this subject based on the ideXlab platform.
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THE WEIGHTED GENERALIZED Gamma Distribution IS THE GENERALIZED Gamma Distribution
Probability in the Engineering and Informational Sciences, 2016Co-Authors: Saralees NadarajahAbstract:It is pointed out that the weighted generalized Gamma Distribution of Priyadarshani and Oluyede [2] is a generalized Gamma Distribution.
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An extension of the Gamma Distribution
Communications in Statistics - Theory and Methods, 2015Co-Authors: Rodrigo R. Pescim, Saralees NadarajahAbstract:ABSTRACTThe Gamma Distribution has been widely used in many research areas such as engineering and survival analysis. We present an extension of this Distribution, called the Kummer beta Gamma Distribution, having greater flexibility to model scenarios involving skewed data. We derive analytical expressions for some mathematical quantities. The estimation of parameters is approached by the maximum likelihood method and Bayesian analysis. The likelihood ratio and formal goodness-of-fit tests are used to compare the presented Distribution with some of its sub-models and non nested models. A real data set is used to illustrate the importance of the Distribution.
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on the compound poisson Gamma Distribution
Kybernetika, 2011Co-Authors: Christopher S Withers, Saralees NadarajahAbstract:The compound Poisson-Gamma variable is the sum of a random sample from a Gamma Distribution with sample size an independent Poisson random variable. It has received wide ranging applications. In this note, we give an account of its mathematical properties including estimation procedures by the methods of moments and maximum likelihood. Most of the properties given are hitherto unknown.
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On the use of the generalised Gamma Distribution
International Journal of Electronics, 2008Co-Authors: Saralees NadarajahAbstract:Some incorrect references are pointed out with respect to the use of the generalised Gamma Distribution in electrical and electronic engineering. The basic mathematical properties of this Distribution are reviewed. It is expected that this note will be useful for the appropriate use of the generalised Gamma Distribution in the IEEE literature.
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A note on the correlated Gamma Distribution of Loaiciga and Leipnik
Advances in Water Resources, 2007Co-Authors: Saralees Nadarajah, Samuel KotzAbstract:Abstract The recent paper by Loaiciga and Leipnik [Loaiciga HA, Leipnik RB. Correlated Gamma variables in the analysis of microbial densities in water. Adv Water Resour 2005;28:329–35] introduced a novel bivariate Gamma Distribution and studied its ratio Distribution with application to hydrological sciences. In this note, we derive the corresponding Distributions of the sum and the product. We also derive a powerful mixture representation of the bivariate Gamma Distribution unnoticed by Loaiciga and Leipnik.
