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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국자료분석학회 Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제17권 제3호
- 발행연도
- 2015.1
- 수록면
- 1,145 - 1,154 (10page)
이용수
초록· 키워드
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Many penalized estimation methods have been applied to the sparse high-dimensional logistic regression models for the classification tasks. Standard convex penalties such as the least absolute shrinkage and selection operator (LASSO) and ridge are known to have high prediction accuracy but tend to select too many irrelevant variables. On the other hand, standard concave penalties such as the smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP) are known to successfully delete irrelevant variables but tend to be inferior to convex penalties in prediction accuracy. In this paper, we study a new penalty, the moderately clipped LASSO (MCL), in the sparse high-dimensional logistic regression models. The MCL is designed by combining the LASSO and MCP, and hence it is expected to preserve advantages of the LASSO and MCP simultaneously. We develop an efficient algorithm also. Numerical studies using simulated and real data analysis show that the MCL method successfully deletes irrelevant variables without losing prediction accuracy.
목차
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참고문헌 (27)
참고문헌 신청
Breheny, P. / 2011 / Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection / Annals of Applied Statistics 5 : 23
Breiman, L. / 1996 / Heuristics of instability and stabilization in model selection / Annals of Statistics 24 : 2350 ~ 2383
Dettling, M. / 2002 / Supervised clustering of genes / Genome Biology 3 : 1 ~ 69
Dudoit, S. / 2002 / Comparison of discrimination methods for the classification of tumors using gene expression data / Journal of the American Statistical Association 97 : 77
Fan, J. / 2001 / Variable selection via nonconcave penalized likelihood and its oracle properties / Journal of the American Statistical Association 96 : 1348 ~ 1360
Breiman, L. / 1996 / Heuristics of instability and stabilization in model selection / Annals of Statistics 24 : 2350 ~ 2383
Dettling, M. / 2002 / Supervised clustering of genes / Genome Biology 3 : 1 ~ 69
Dudoit, S. / 2002 / Comparison of discrimination methods for the classification of tumors using gene expression data / Journal of the American Statistical Association 97 : 77
Fan, J. / 2001 / Variable selection via nonconcave penalized likelihood and its oracle properties / Journal of the American Statistical Association 96 : 1348 ~ 1360
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