Author(s)

Ning Mao, Wanzhou Ye

Department of Mathematics, College of Science, Shanghai University, Shanghai, China.

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 L_q (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.

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

Mao, N. and Ye, W. (2017) Group Variable Selection via a Combination of L_q Norm and Correlation-Based Penalty. Advances in Pure Mathematics, 7, 51-65. doi: 10.4236/apm.2017.71005.