  The Generalized Linear Model (GLM) for the Gamma distribution (glmGamma) is
widely used in modeling continuous, non-negative and positive-skewed data, such
as insurance claims and survival data. However, model selection for GLM depends
on AIC/BIC criteria, which is computationally impractical for even a moderate
number of variables. In this paper, we develop variable selection for glmGamma
using elastic net regularization (glmGammaNet), for which we provide an
algorithm and implementation. The glmGammaNet model is more challening than
other more common GLMs as the likelihood function has no global quadratic upper
bound, and we develop an efficient accelerated proximal gradient algorithm
using a local model. We report simulation study results and discuss the choice
of regularization parameter. The method is implemented in the R package
glmGammaNet.
