메뉴 건너뛰기




Volumn 140, Issue 5, 2010, Pages 1125-1134

Nonlinear regression modeling via the lasso-type regularization

Author keywords

Basis expansion; Bayes approach; Information criterion; Lasso; Nonlinear regression; Regularization

Indexed keywords


EID: 74149088936     PISSN: 03783758     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jspi.2009.10.015     Document Type: Article
Times cited : (23)

References (22)
  • 1
    • 49049090541 scopus 로고    scopus 로고
    • Nonlinear regression modeling via regularized radial basis function networks
    • Ando T., Konishi S., and Imoto S. Nonlinear regression modeling via regularized radial basis function networks. J. Stat. Planning Inference 138 (2008) 3616-3633
    • (2008) J. Stat. Planning Inference , vol.138 , pp. 3616-3633
    • Ando, T.1    Konishi, S.2    Imoto, S.3
  • 4
    • 34548275795 scopus 로고    scopus 로고
    • The Dantzig selector: statistical estimation when p is much larger than n (with discussion)
    • Candes E., and Tao T. The Dantzig selector: statistical estimation when p is much larger than n (with discussion). Ann. Statist. 35 (2007) 2313-2351
    • (2007) Ann. Statist. , vol.35 , pp. 2313-2351
    • Candes, E.1    Tao, T.2
  • 6
    • 1542784498 scopus 로고    scopus 로고
    • Variable selection via nonconcave penalized likelihood and its oracle properties
    • Fan J., and Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. J. Amer. Statist. Assoc. 96 (2001) 1348-1360
    • (2001) J. Amer. Statist. Assoc. , vol.96 , pp. 1348-1360
    • Fan, J.1    Li, R.2
  • 7
    • 84952149204 scopus 로고
    • A statistical view of some chemometrics regression tools
    • Frank I.E., and Friedman J.H. A statistical view of some chemometrics regression tools. Technometrics 35 (1993) 109-148
    • (1993) Technometrics , vol.35 , pp. 109-148
    • Frank, I.E.1    Friedman, J.H.2
  • 8
    • 0032361278 scopus 로고    scopus 로고
    • Penalized regression: the bridge versus the lasso
    • Fu W.J. Penalized regression: the bridge versus the lasso. J. Comput. Graph. Statist. 7 (1998) 397-416
    • (1998) J. Comput. Graph. Statist. , vol.7 , pp. 397-416
    • Fu, W.J.1
  • 9
    • 0021518209 scopus 로고
    • Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
    • Geman S., and Geman D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pat. Anal. Mach. Intel. 6 (1984) 721-741
    • (1984) IEEE Trans. Pat. Anal. Mach. Intel. , vol.6 , pp. 721-741
    • Geman, S.1    Geman, D.2
  • 11
    • 84942484786 scopus 로고
    • Ridge regression: biased estimation for nonorthogonal problems
    • Hoerl A.E., and Kennard R.W. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12 (1970) 55-67
    • (1970) Technometrics , vol.12 , pp. 55-67
    • Hoerl, A.E.1    Kennard, R.W.2
  • 12
    • 3242876379 scopus 로고    scopus 로고
    • Selection of smoothing parameter in B-spline non-parametric regression models using information criteria
    • Imoto S., and Konishi S. Selection of smoothing parameter in B-spline non-parametric regression models using information criteria. Ann. Inst. Statist. Math. 55 (2003) 671-687
    • (2003) Ann. Inst. Statist. Math. , vol.55 , pp. 671-687
    • Imoto, S.1    Konishi, S.2
  • 13
    • 3242882795 scopus 로고    scopus 로고
    • Bayesian information criteria and smoothing parameter selection in radial basis function network
    • Konishi S., Ando T., and Imoto S. Bayesian information criteria and smoothing parameter selection in radial basis function network. Biometrika 91 (2004) 27-43
    • (2004) Biometrika , vol.91 , pp. 27-43
    • Konishi, S.1    Ando, T.2    Imoto, S.3
  • 14
    • 0000512689 scopus 로고    scopus 로고
    • Generalised information criteria in model selection
    • Konishi S., and Kitagawa G. Generalised information criteria in model selection. Biometrika 83 (1996) 875-890
    • (1996) Biometrika , vol.83 , pp. 875-890
    • Konishi, S.1    Kitagawa, G.2
  • 16
    • 0000672424 scopus 로고
    • Fast learning in networks of locally-turned processing units
    • Moody J., and Darken C.J. Fast learning in networks of locally-turned processing units. Neural Computation 1 (1989) 281-294
    • (1989) Neural Computation , vol.1 , pp. 281-294
    • Moody, J.1    Darken, C.J.2
  • 18
    • 46749151407 scopus 로고    scopus 로고
    • Weighted lasso in graphical Gaussian modeling for large gene network estimation based on microarray data
    • Shimamura T., Imoto S., Yamaguchi R., and Miyano S. Weighted lasso in graphical Gaussian modeling for large gene network estimation based on microarray data. Genome Informatics 19 (2007) 142-153
    • (2007) Genome Informatics , vol.19 , pp. 142-153
    • Shimamura, T.1    Imoto, S.2    Yamaguchi, R.3    Miyano, S.4
  • 20
    • 0001287271 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the Lasso
    • Tibshirani R.J. Regression shrinkage and selection via the Lasso. J. Roy. Statist. Soc. Ser. B 58 (1996) 267-288
    • (1996) J. Roy. Statist. Soc. Ser. B , vol.58 , pp. 267-288
    • Tibshirani, R.J.1
  • 21
    • 33846114377 scopus 로고    scopus 로고
    • The adaptive lasso and its oracle properties
    • Zou H. The adaptive lasso and its oracle properties. J. Amer. Statist. Assoc. 101 (2006) 1418-1429
    • (2006) J. Amer. Statist. Assoc. , vol.101 , pp. 1418-1429
    • Zou, H.1
  • 22
    • 16244401458 scopus 로고    scopus 로고
    • Regularization and variable selection via the elastic net
    • Zou H., and Hastie T. Regularization and variable selection via the elastic net. J. Roy. Statist. Soc. Ser. B 67 (2005) 301-320
    • (2005) J. Roy. Statist. Soc. Ser. B , vol.67 , pp. 301-320
    • Zou, H.1    Hastie, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.