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This paper is the generalization of weight-fused elastic net (Fu and Xu, 2012), which performs group variable selection by combining weight-fused LASSO(wfLasso) and elastic net (Zou and Hastie, 2005) penalties. In this study, the elastic net penalty is replaced by adaptive elastic net penalty (AdaEnet) (Zou and Zhang, 2009), and a new group variable selection algorithm with oracle property (Fan and Li, 2001; Zou, 2006) is obtained. [ABSTRACT FROM PUBLISHER]

In this article, we consider the problem of variable selection and estimation with the strongly correlated multi-collinear data by using grouping variable selection techniques. A new grouping variable selection method, called weight-fused elastic net(WFEN), is proposed to deal with the high dimensional collinear data. The proposed model, combined two different grouping effect mechanisms induced by the elastic net and weight-fused LASSO, respectively, can be easily unified in the frame of LASSO and computed efficiently. The performance with the simulation and real data sets shows that our method is competitive with other related methods, especially when the data present high multi-collinearity. [ABSTRACT FROM PUBLISHER]