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Scale Mixture of Rayleigh Distribution
by
Barranco-Chamorro, Inmaculada
, Rivera, Pilar A.
, Gómez, Héctor W.
, Gallardo, Diego I.
in
Algorithms
/ EM algorithm
/ Independent variables
/ Kurtosis
/ Mathematics
/ maximum likelihood
/ Maximum likelihood estimates
/ Parameter estimation
/ Probability distribution functions
/ Quotients
/ Random variables
/ Rayleigh distribution
/ Rényi entropy
/ Simulation
/ slashed Rayleigh distribution
/ Survival analysis
2020
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Scale Mixture of Rayleigh Distribution
by
Barranco-Chamorro, Inmaculada
, Rivera, Pilar A.
, Gómez, Héctor W.
, Gallardo, Diego I.
in
Algorithms
/ EM algorithm
/ Independent variables
/ Kurtosis
/ Mathematics
/ maximum likelihood
/ Maximum likelihood estimates
/ Parameter estimation
/ Probability distribution functions
/ Quotients
/ Random variables
/ Rayleigh distribution
/ Rényi entropy
/ Simulation
/ slashed Rayleigh distribution
/ Survival analysis
2020
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Do you wish to request the book?
Scale Mixture of Rayleigh Distribution
by
Barranco-Chamorro, Inmaculada
, Rivera, Pilar A.
, Gómez, Héctor W.
, Gallardo, Diego I.
in
Algorithms
/ EM algorithm
/ Independent variables
/ Kurtosis
/ Mathematics
/ maximum likelihood
/ Maximum likelihood estimates
/ Parameter estimation
/ Probability distribution functions
/ Quotients
/ Random variables
/ Rayleigh distribution
/ Rényi entropy
/ Simulation
/ slashed Rayleigh distribution
/ Survival analysis
2020
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Journal Article
Scale Mixture of Rayleigh Distribution
2020
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Overview
In this paper, the scale mixture of Rayleigh (SMR) distribution is introduced. It is proven that this new model, initially defined as the quotient of two independent random variables, can be expressed as a scale mixture of a Rayleigh and a particular Generalized Gamma distribution. Closed expressions are obtained for its pdf, cdf, moments, asymmetry and kurtosis coefficients. Its lifetime analysis, properties and Rényi entropy are studied. Inference based on moments and maximum likelihood (ML) is proposed. An Expectation-Maximization (EM) algorithm is implemented to estimate the parameters via ML. This algorithm is also used in a simulation study, which illustrates the good performance of our proposal. Two real datasets are considered in which it is shown that the SMR model provides a good fit and it is more flexible, especially as for kurtosis, than other competitor models, such as the slashed Rayleigh distribution.
Publisher
MDPI AG
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