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https://doi.org/10.1007/s11222-008-9088-5

has been cited by the following article:

• TITLE: Group Variable Selection via a Combination of Lq Norm and Correlation-Based Penalty

  AUTHORS: Ning Mao, Wanzhou Ye

  KEYWORDS: Linear Regression, Variable Selection, Elastic Net, Adaptive Elastic Net

  JOURNAL NAME: Advances in Pure Mathematics, Vol.7 No.1, January 24, 2017

  ABSTRACT: Considering the problem of feature selection in linear regression model, a new method called LqCP is proposed simultaneously to select variables and favor a grouping effect, where strongly correlated predictors tend to be in or out of the model together. LqCP is based on penalized least squares with a penalty function that combines the Lq (0n. In addition, a simulation about grouped variable selection is performed. Finally, The model is applied to two real data: US Crime Data and Gasoline Data. In terms of prediction error and estimation error, empirical studies show the efficiency of LqCP.