Regresión Gamma con el paquete Gammareg en R
Gamma regression models with the Gammareg R package
Abstract (en)
The class of gamma regression models is based on the assumption that the depen- dent variable is gamma distributed and that its mean is related to a set of regressors through a linear predictor with unknown coefficients and a link function. This link can be the identity, the inverse or the logarithm function. The model also includes a shape parameter, which may be constant or dependent on a set of regressors through a link function, as the logarithm function. In this paper we describe the Gammareg R-package, which provides the class of gamma regressions in the R system for their statistical computing. The underlying theory is briefly presented and the library implementation illustrated from simulation studies.
Abstract (es)
En este artículo se presenta el paquete Gammareg, el cual utiliza el método FisherScoring para ajustar modelos de regresión gamma, donde tanto la media como el parámetro de forma poseen estructuras de regresión, y el cual fue desarrollado en el software R. Después de realizar una breve presentación de la teoría subyacente, se presenta el uso de la librería por medio de estudios de simulación y aplicaciones.
References
Bateson, T. F. (2009), `Gamma regression of interevent waiting times versus pois-son regression of daily event counts: Inside the epidemiologist's toolbox selec-ting the best modeling tools for the job',Epidemiology 20(2), 202-204.
Cepeda-Cuervo, E. (2001), `Modelagem de variabilidade em modelos lineares generalizados', Unpublished Ph.D.thesis, Mathematics Institute, Universidad Federal Rio de Janeiro.
Cepeda, E. & Gammerman, D. (2005), `Bayesian methodology for modeling parameters in the two parameters exponential family',ESTADISTICA57(168), 93-105.
Chib, S. & Greenberg, E. (1995), `Understanding the metropolis-hastings algorithm', The American Statistician49(4), 327-335.
Gamerman, D. & Lopes, H. F. (2006),Markov chain Monte Carlo: Stochasticsimulation for Bayesian inference, CRC Press, address=New York,.
Krishnamoorthy, K. (2006), Handbook of Statistical Distributions with Applica-tions, Chapman & Hall/CRC, Florida.
McCullagh, J. & Nelder, J. (1989), Generalized Linear Models. Second Edition, Chapman and Hall, London.
Team., D. C. (n.d.), A language and environment for statistical computing. R Foundation for Statistical Computing.
Winklemann, R. (2008), Econometric analysis of count data, Springer-Verlag, Berlin, Germany.
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