• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.615-624
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• 2005

In this paper, we review the variable-selection properties of LASSO and SCAD in penalized regression. To improve the weakness of SCAD for high noise level, we propose a new penalty function called MSCAD which relaxes the unbiasedness condition of SCAD. In order to compare MSCAD with LASSO and SCAD, comparative studies are performed on simulated datasets and also on a real dataset. The performances of penalized regression methods are compared in terms of relative model error and the estimates of coefficients. The results of experiments show that the performance of MSCAD is between those of LASSO and SCAD as expected.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.7-12
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• 2005

In this paper, we review the variable-selection properties of LASSO and SCAD in penalized regression. To improve the weakness of SCAD for high noise level, we propose a new penalty function called MSCAD which relaxes the unbiasedness condition of SCAD. In order to compare MSCAD with LASSO and SCAD, comparative studies are performed on simulated datasets and also on a real dataset. The performances of penalized regression methods are compared in terms of relative model error and the estimates of coefficients. The results of experiments show that the performance of MSCAD is between those of LASSO and SCAD as expected.