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Volumn 36, Issue 5, 2008, Pages 2207-2231

Gibbs posterior for variable selection in high-dimensional classification and data mining

Author keywords

Data augmentation; Data mining; Gibbs posterior; High dimensional data; Linear classification; Markov chain Monte Carlo; Prior distribution; Risk performance; Sparsity; Variable selection

Indexed keywords


EID: 54349112850     PISSN: 00905364     EISSN: None     Source Type: Journal    
DOI: 10.1214/07-AOS547     Document Type: Article
Times cited : (117)

References (20)
  • 1
    • 0000294132 scopus 로고    scopus 로고
    • The choice of variables in multivariate regression: A non-conjugate Bayesian decision theory approach
    • BROWN, P. J., FEARN, T. and VANNUCCI, M. (1999). The choice of variables in multivariate regression: A non-conjugate Bayesian decision theory approach. Biometrika 86 635-648.
    • (1999) Biometrika , vol.86 , pp. 635-648
    • BROWN, P.J.1    FEARN, T.2    VANNUCCI, M.3
  • 5
    • 0021518209 scopus 로고
    • Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
    • GEMAN, S. and GEMAN, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Machine Intell. 6 721-741.
    • (1984) IEEE Trans. Pattern Anal. Machine Intell , vol.6 , pp. 721-741
    • GEMAN, S.1    GEMAN, D.2
  • 6
    • 0031526204 scopus 로고    scopus 로고
    • Approaches for Bayesian variable selection
    • GEORGE, E. I. and MCCULLOCH, R. E. (1997). Approaches for Bayesian variable selection. Statist. Sinica 7 339-373.
    • (1997) Statist. Sinica , vol.7 , pp. 339-373
    • GEORGE, E.I.1    MCCULLOCH, R.E.2
  • 7
    • 0036000545 scopus 로고    scopus 로고
    • Bayesian variable selection in logistic regression: Predicting company earnings direction
    • GERLACH, R., BIRD, R. and HALL, A. (2002). Bayesian variable selection in logistic regression: Predicting company earnings direction. Aust. N. Z. J. Statist. 44 155-168.
    • (2002) Aust. N. Z. J. Statist , vol.44 , pp. 155-168
    • GERLACH, R.1    BIRD, R.2    HALL, A.3
  • 9
    • 0000444966 scopus 로고
    • A smoothed maximum score estimator for the binary response model
    • HOROWITZ, J. L. (1992). A smoothed maximum score estimator for the binary response model. Econometrica 60 505-531.
    • (1992) Econometrica , vol.60 , pp. 505-531
    • HOROWITZ, J.L.1
  • 10
    • 33746227433 scopus 로고    scopus 로고
    • Misspecification in infinite-dimensional Bayesian statistics
    • KLEIJN, B. J. K. and VAN DER VAART, A. W. (2006). Misspecification in infinite-dimensional Bayesian statistics. Ann. Statist. 34 837-877.
    • (2006) Ann. Statist , vol.34 , pp. 837-877
    • KLEIJN, B.J.K.1    VAN DER VAART, A.W.2
  • 11
    • 50449090913 scopus 로고    scopus 로고
    • Bayesian variable selection for high dimensional generalized linear models: Convergence rates of the fitted densities
    • JIANG, W. (2007). Bayesian variable selection for high dimensional generalized linear models: Convergence rates of the fitted densities. Ann. Statist. 35 1487-1511.
    • (2007) Ann. Statist , vol.35 , pp. 1487-1511
    • JIANG, W.1
  • 13
    • 0002438052 scopus 로고
    • The choice of variables in multiple regression (with discussion)
    • LINDLEY, D. V. (1968). The choice of variables in multiple regression (with discussion). J. Roy. Statist. Assoc. Ser. B 30 31-66.
    • (1968) J. Roy. Statist. Assoc. Ser. B , vol.30 , pp. 31-66
    • LINDLEY, D.V.1
  • 14
    • 0000824232 scopus 로고    scopus 로고
    • Nonparametric regression using Bayesian variable selection
    • SMITH, M. and KOHN, R. (1996). Nonparametric regression using Bayesian variable selection. J. Econometrics 75 317-343.
    • (1996) J. Econometrics , vol.75 , pp. 317-343
    • SMITH, M.1    KOHN, R.2
  • 16
    • 84950758368 scopus 로고
    • The calculation of posterior distributions by data augmentation (with discussion)
    • TANNER, M. A. and WONG, W. H. (1987). The calculation of posterior distributions by data augmentation (with discussion). J. Amer. Statist. Assoc. 82 528-550.
    • (1987) J. Amer. Statist. Assoc , vol.82 , pp. 528-550
    • TANNER, M.A.1    WONG, W.H.2
  • 18
    • 33847361463 scopus 로고    scopus 로고
    • From ε-entropy to KL-entropy: Analysis of minimum information complexity density estimation
    • ZHANG, T. (2006a). From ε-entropy to KL-entropy: Analysis of minimum information complexity density estimation. Ann. Statist. 34 2180-2210.
    • (2006) Ann. Statist , vol.34 , pp. 2180-2210
    • ZHANG, T.1
  • 19
    • 33645722194 scopus 로고    scopus 로고
    • Information theoretical upper and lower bounds for statistical estimation
    • ZHANG, T. (2006b). Information theoretical upper and lower bounds for statistical estimation. IEEE Trans. Inform. Theory 52 1307-1321.
    • (2006) IEEE Trans. Inform. Theory , vol.52 , pp. 1307-1321
    • ZHANG, T.1
  • 20
    • 4744364173 scopus 로고    scopus 로고
    • Cancer classification and prediction using logistic regression with Bayesian gene selection
    • ZHOU, X., LIU, K.-Y. and WONG, S. T. C. (2004). Cancer classification and prediction using logistic regression with Bayesian gene selection. J. Biomedical Informatics 37 249-259.
    • (2004) J. Biomedical Informatics , vol.37 , pp. 249-259
    • ZHOU, X.1    LIU, K.-Y.2    WONG, S.T.C.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.