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Volumn 28, Issue 3, 2012, Pages 722-738

Fast sparse regression and classification

Author keywords

Bridge regression; Classification; Elastic net; L p norm penalization; Lasso; Regression; Regularization; Sparsity; Variable selection

Indexed keywords


EID: 84862556734     PISSN: 01692070     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijforecast.2012.05.001     Document Type: Article
Times cited : (191)

References (30)
  • 2
    • 84874257732 scopus 로고
    • Better subset regression using the nonnegative garrote
    • Breiman L. Better subset regression using the nonnegative garrote. Technometrics 1995, 37:373-384.
    • (1995) Technometrics , vol.37 , pp. 373-384
    • Breiman, L.1
  • 4
    • 34548275795 scopus 로고    scopus 로고
    • The Dantzig selector: statistical estimation when p is much larger than n (with discussion)
    • Candes E., Tao T. The Dantzig selector: statistical estimation when p is much larger than n (with discussion). Annals of Statistics 2007, 35:2313-2351.
    • (2007) Annals of Statistics , vol.35 , pp. 2313-2351
    • Candes, E.1    Tao, T.2
  • 5
    • 7044231546 scopus 로고    scopus 로고
    • An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
    • Daubechines I., DeFrise M., De Mol C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Communications on Pure and Applied Mathematics 2004, 57:1413-1457.
    • (2004) Communications on Pure and Applied Mathematics , vol.57 , pp. 1413-1457
    • Daubechines, I.1    DeFrise, M.2    De Mol, C.3
  • 7
    • 1542784498 scopus 로고    scopus 로고
    • Variable selection via nonconcave penalized likelihood and its oracle properties
    • Fan J., Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association 2001, 96:1348-1360.
    • (2001) Journal of the American Statistical Association , vol.96 , pp. 1348-1360
    • Fan, J.1    Li, R.2
  • 8
    • 84952149204 scopus 로고
    • A statistical view of some chemometrics regression tools (with discussion)
    • Frank I.E., Friedman J.H. A statistical view of some chemometrics regression tools (with discussion). Technometrics 1993, 35:109-148.
    • (1993) Technometrics , vol.35 , pp. 109-148
    • Frank, I.E.1    Friedman, J.H.2
  • 9
    • 0035470889 scopus 로고    scopus 로고
    • Greedy function approximation: a gradient boosting machine
    • Friedman J.H. Greedy function approximation: a gradient boosting machine. Annals of Statistics 2001, 29:1189-1232.
    • (2001) Annals of Statistics , vol.29 , pp. 1189-1232
    • Friedman, J.H.1
  • 11
    • 84862552569 scopus 로고    scopus 로고
    • Regularized paths for generalized linear models via coordinate descent. Stanford University, Dept. of Statistics technical report.
    • Friedman, J. H., Hastie, T., & Tibshirani, R. (2008). Regularized paths for generalized linear models via coordinate descent. Stanford University, Dept. of Statistics technical report.
    • (2008)
    • Friedman, J.H.1    Hastie, T.2    Tibshirani, R.3
  • 12
    • 34548105186 scopus 로고    scopus 로고
    • Large-scale Bayesian logistic regression for text categorization
    • Genkin A., Lewis D., Madigan D. Large-scale Bayesian logistic regression for text categorization. Technometrics 2007, 49:291-304.
    • (2007) Technometrics , vol.49 , pp. 291-304
    • Genkin, A.1    Lewis, D.2    Madigan, D.3
  • 15
    • 84942484786 scopus 로고
    • Ridge regression: biased estimation for nonorthogonal problems
    • Horel A.E., Kennard R.W. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 1970, 12:55-67.
    • (1970) Technometrics , vol.12 , pp. 55-67
    • Horel, A.E.1    Kennard, R.W.2
  • 16
    • 84862549237 scopus 로고    scopus 로고
    • A group bridge approach for variable selection. The University of Iowa, Dept. of Statistics technical report, No. 376.
    • Huang, J., Ma, S., Xie, H., & Zhang, C.-H. (2007). A group bridge approach for variable selection. The University of Iowa, Dept. of Statistics technical report, No. 376.
    • (2007)
    • Huang, J.1    Ma, S.2    Xie, H.3    Zhang, C.-H.4
  • 22
    • 84862552566 scopus 로고    scopus 로고
    • Topics in regularization and boosting. Ph.D. Thesis, Dept. of Statistics, Stanford University.
    • Rosset, S. (2003). Topics in regularization and boosting. Ph.D. Thesis, Dept. of Statistics, Stanford University.
    • (2003)
    • Rosset, S.1
  • 24
    • 42149190756 scopus 로고    scopus 로고
    • Prediction accuracy and stability of regression with optimal scaling transformations.
    • Ph.D. Thesis, Dept. of Data Theory, Leiden University.
    • Van der Kooij, A. (2007). Prediction accuracy and stability of regression with optimal scaling transformations. Ph.D. Thesis, Dept. of Data Theory, Leiden University.
    • (2007)
    • Van der Kooij, A.1
  • 25
    • 0001681052 scopus 로고
    • The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses
    • Wold S., Ruhe A., Wold H., Dunn III W.J. The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM Journal of Scientific and Statistical Computing 1984, 5:735-742.
    • (1984) SIAM Journal of Scientific and Statistical Computing , vol.5 , pp. 735-742
    • Wold, S.1    Ruhe, A.2    Wold, H.3    Dunn III, W.J.4
  • 26
    • 84863879353 scopus 로고    scopus 로고
    • Coordinate descent algorithms for lasso penalized regression
    • Wu T., Lange K. Coordinate descent algorithms for lasso penalized regression. Annals of Applied Statistics 2008, 2:224-244.
    • (2008) Annals of Applied Statistics , vol.2 , pp. 224-244
    • Wu, T.1    Lange, K.2
  • 27
  • 28
    • 84862555987 scopus 로고    scopus 로고
    • Penalized linear unbiased selection. Rutgers University, Dept. of Statistics technical report, No. 2007-003.
    • Zhang, C.-H. (2007). Penalized linear unbiased selection. Rutgers University, Dept. of Statistics technical report, No. 2007-003.
    • (2007)
    • Zhang, C.-H.1
  • 29
    • 84862549235 scopus 로고    scopus 로고
    • Grouped and hierarchical model selection through composite absolute penalties. University of California, Berkeley, Dept. of Statistics technical report, No. 703.
    • Zhao, P., Rocha, G., & Yu, B. (2006). Grouped and hierarchical model selection through composite absolute penalties. University of California, Berkeley, Dept. of Statistics technical report, No. 703.
    • (2006)
    • Zhao, P.1    Rocha, G.2    Yu, B.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.