A Bayesian Approach to Mixed Gamma Regression Models

Gamma regression models are a suitable choice to model continuous variables that take positive real values. This paper presents a gamma regression model with mixed effects from a Bayesian approach. We use the parametrisation of the gamma distribution in terms of the mean and the shape parameter, bot...

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Bibliographic Details
Published in:Revista Colombiana de estadística 2019, Vol.42 (1), p.81-99
Main Authors: Corrales, Marta Lucia, Cepeda-Cuervo, Edilberto
Format: Article
Language:English
Subjects:
Bayesian analysis
Computer simulation
Continuity (mathematics)
Economic models
Parameterization
Probability distribution functions
Real variables
Regression analysis
Regression models
Statistical analysis
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Summary:Gamma regression models are a suitable choice to model continuous variables that take positive real values. This paper presents a gamma regression model with mixed effects from a Bayesian approach. We use the parametrisation of the gamma distribution in terms of the mean and the shape parameter, both of which are modelled through regression structures that may involve fixed and random effects.  A computational implementation via Gibbs sampling is provided and illustrative examples (simulated and real data) are presented.
ISSN:0120-1751
2389-8976
DOI:10.15446/rce.v42n1.69334