A differential-geometric approach to generalized linear models with grouped predictors
提出一种微分几何最小角回归方法，用于广义线性模型中的分组稀疏推断，其解曲线基于模型不变性，分组等角条件与得分统计量相关，自适应版本具有Oracle性质。
We propose an extension of the differential-geometric least angle regression method to perform 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 statistics. 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.