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Volumn 9, Issue , 2010, Pages 381-388

Learning exponential families in high-dimensions: Strong convexity and sparsity

Author keywords

[No Author keywords available]

Indexed keywords

CONVEXITY PROPERTIES; EXPONENTIAL FAMILY; GENERALIZATION ABILITY; HIGH DIMENSIONS; L1 REGULARIZATIONS; OPTIMAL PARAMETER; STATISTICAL MODELS;

EID: 84861312320     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (43)

References (15)
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    • Francis Bach. Self-concordant analysis for logistic regression. CoRR, abs/0910.4627, 2009.
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    • Simultaneous analysis of Lasso and Dantzig selector
    • Peter J. Bickel, Ya'acov Ritov, and Alexandre B. Tsybakov. Simultaneous analysis of Lasso and Dantzig selector. Annals of Statistics, 37(4):1705-1732, 2008.
    • (2008) Annals of Statistics , vol.37 , Issue.4 , pp. 1705-1732
    • Bickel, P.J.1    Ritov, Y.2    Tsybakov, A.B.3
  • 5
    • 34548275795 scopus 로고    scopus 로고
    • The Dantzig selector: Statistical estimation when p is much larger than n
    • Emmanuel Candes and Terence Tao. The Dantzig selector: Statistical estimation when p is much larger than n. Annals of Statistics, 35:2313, 2007.
    • (2007) Annals of Statistics , vol.35 , pp. 2313
    • Candes, E.1    Tao, T.2
  • 7
    • 0347117630 scopus 로고    scopus 로고
    • Asymptotic normality of posterior distributions for exponential families when the number of parameters tends to infinity
    • ISSN 0047-259X
    • Subhashis Ghosal. Asymptotic normality of posterior distributions for exponential families when the number of parameters tends to infinity. J. Multivar. Anal., 74(1): 49-68, 2000. ISSN 0047-259X.
    • (2000) J. Multivar. Anal. , vol.74 , Issue.1 , pp. 49-68
    • Ghosal, S.1
  • 8
    • 65349193793 scopus 로고    scopus 로고
    • Lasso-type recovery of sparse representations for high-dimensional data
    • Nicolai Meinshausen and Bin Yu. Lasso-type recovery of sparse representations for high-dimensional data. Annals of Statistics, 37:246, 2009.
    • (2009) Annals of Statistics , vol.37 , pp. 246
    • Meinshausen, N.1    Yu, B.2
  • 9
    • 84858717588 scopus 로고    scopus 로고
    • A unified framework for high-dimensional analysis of m-estimators with decomposable regularizers
    • S. Negahban, P. Ravikumar, M. Wainwright, and B. Yu. A unified framework for high-dimensional analysis of m-estimators with decomposable regularizers. In NIPS, 2009.
    • (2009) NIPS
    • Negahban, S.1    Ravikumar, P.2    Wainwright, M.3    Yu, B.4
  • 10
    • 0000791208 scopus 로고
    • Asymptotic behavior of likelihood methods for exponential families when the number of parameters tends to infinity
    • S. Portnoy. Asymptotic behavior of likelihood methods for exponential families when the number of parameters tends to infinity. Annals of Statistics, 16, 1988.
    • (1988) Annals of Statistics , vol.16
    • Portnoy, S.1
  • 13
    • 51049121146 scopus 로고    scopus 로고
    • High-dimensional generalized linear models and the lasso
    • Sara A. van de Geer. High-dimensional generalized linear models and the lasso. Annals of Statistics, 36(2):614-645, 2008.
    • (2008) Annals of Statistics , vol.36 , Issue.2 , pp. 614-645
    • Van De Geer, S.A.1
  • 14
    • 84863393425 scopus 로고    scopus 로고
    • Adaptive forward-backward greedy algorithm for sparse learning with linear models
    • T. Zhang. Adaptive forward-backward greedy algorithm for sparse learning with linear models. In Advances in Neural Information Processing Systems 22, 2008.
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    • Zhang, T.1
  • 15


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.