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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 제16권 제4호
- 발행연도
- 2014.1
- 수록면
- 1,715 - 1,725 (11page)
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초록· 키워드
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In this paper, we study a new penalized maximum likelihood estimator, the sparse ridge, for the sparse logistic regression model. Here, the sparsity of the logistic model implies that the model includes some irrelevant predictive variables. Hence, by deleting the irrelevant predictive variables from the model, we want to identify a correct sub-model, enhancing both prediction accuracy and model interpretability. For the problem, the sparse ridge gives a simultaneous process of selecting relevant predictive variables and estimating included parameters. First, the sparse ridge achieves a sparsity as usual penalized maximum likelihood estimators such as the least absolute selection and shrinkage operator and smoothly clipped absolute deviation penalized estimators. Hence we can use the sparse ridge for variable selection for the sparse logistic regression model. Further, the sparse ridge shares a common shrinkage effect in parameter estimation with the ridge. Hence the sparse ridge is expected to perform well under the multi-collinearity, especially. Numerical studies via simulated and real data analysis show that the sparse ridge can be a nice alternative to the well-known penalized maximum likelihood estimators.
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참고문헌 (31)
참고문헌 신청
연규필 / 2010 / 판정보류 옵션을 가진 분류자 / Journal of The Korean Data Analysis Society 12(6):3317~3325
Yuille, A. / 2003 / The concave-convex procedure / Neural Computation 15:915~936
Zhao, P. / 2006 / On model selection consistency of LASSO / Journal of the Machine Learning Research 7:2541~2563
Zou, H. / 2005 / Regularization and variable selection via the elastic net / Journal of Royal Statistical Society, Series B 67:301~320
Alon, U. / 1999 / Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays / Proceedings of the National
Yuille, A. / 2003 / The concave-convex procedure / Neural Computation 15:915~936
Zhao, P. / 2006 / On model selection consistency of LASSO / Journal of the Machine Learning Research 7:2541~2563
Zou, H. / 2005 / Regularization and variable selection via the elastic net / Journal of Royal Statistical Society, Series B 67:301~320
Alon, U. / 1999 / Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays / Proceedings of the National
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