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The Generalized Linear Model (GLM) for the Gamma distribution (glmGamma) iswidely used in modeling continuous, non-negative and positive-skewed data, suchas insurance claims and survival data. However, model selection for GLM dependson AIC/BIC criteria, which is computationally impractical for even a moderatenumber of variables. In this paper, we develop variable selection for glmGammausing elastic net regularization (glmGammaNet), for which we provide analgorithm and implementation. The glmGammaNet model is more challening thanother more common GLMs as the likelihood function has no global quadratic upperbound, and we develop an efficient accelerated proximal gradient algorithmusing a local model. We report simulation study results and discuss the choiceof regularization parameter. The method is implemented in the R packageglmGammaNet.

Generalized Linear Model for Gamma Distributed Variables via Elastic Net Regularization