Variable selection is fundamental to statistical modeling. Many approaches in use are stepwise selection procedures, such as best subset variable selection and stepwise backward elimination, which can be expensive in computation and ignore stochastic errors in the variable selection process. In This email address is being protected from spam bots, you need Javascript enabled to view it work, new approaches such as the SCAD penalty are proposed to select significant variables for various statistical models. Based on penalized likelihood, the proposed approaches delete insignificant covariates by estimating their coefficients to be zero, and therefore simultaneously select significant variables and estimate parameters. It has been shown in this work that the proposed approaches have oracle properties, namely, they work as well as if the correct submodel were known.

The Methodology Center has developed software for applying the SCAD penalty within SAS.

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