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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.

advances in pure mathematics

Group Variable Selection via a Combination of &amp;lt;i&amp;gt;L&amp;lt;/i&amp;gt;&amp;lt;sub&amp;gt;q&amp;lt;/sub&amp;gt; Norm and Correlation-Based Penalty