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A differential-geometric approach to generalized linear models with grouped predictors

  • Autori: Augugliaro, L.; Mineo, A.; Wit, E.
  • Anno di pubblicazione: 2016
  • Tipologia: Articolo in rivista (Articolo in rivista)


We propose an extension of the differential-geometric least angle regression method to per- form sparse group inference in a generalized linear model. An efficient algorithm is proposed to compute the solution curve. The proposed group differential-geometric least angle regression method has important properties that distinguish it from the group lasso. First, its solution curve is based on the invariance properties of a generalized linear model. Second, it adds groups of variables based on a group equiangularity condition, which is shown to be related to score statis- tics. An adaptive version, which includes weights based on the Kullback–Leibler divergence, improves its variable selection features and is shown to have oracle properties when the number of predictors is fixed.