摘要
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.
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.
