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Variable Selection Via Penalized Regression
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 Title & Authors
Variable Selection Via Penalized Regression
Yoon, Young-Joo; Song, Moon-Sup;
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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.
Penalized regression;Penalty function;LASSO;SCAD;MSCAD;
 Cited by
공분산분석 모형에서의 변수선택 정리,윤상후;박정수;

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