메뉴 건너뛰기




Volumn 38, Issue 2, 2010, Pages 704-752

Goodness-of-fit tests for high-dimensional Gaussian linear models

Author keywords

Adaptive testing; Ellipsoid; Gaussian graphical models; Goodness of fit; Linear regression; Minimax hypothesis testing; Minimax separation rate; Multiple testing

Indexed keywords


EID: 77649319133     PISSN: 00905364     EISSN: None     Source Type: Journal    
DOI: 10.1214/08-AOS629     Document Type: Article
Times cited : (27)

References (30)
  • 2
    • 24344470806 scopus 로고    scopus 로고
    • Non-asymptotic rates of testing in signal detection
    • BARAUD, Y. (2002). Non-asymptotic rates of testing in signal detection. Bernoulli 8 577-606.
    • (2002) Bernoulli , vol.8 , pp. 577-606
    • Baraud, Y.1
  • 3
    • 21144438312 scopus 로고    scopus 로고
    • Adaptative tests of linear hypotheses by model selection
    • BARAUD, Y., HUET, S. and LAURENT, B. (2003). Adaptative tests of linear hypotheses by model selection. Ann. Statist. 31 225-251.
    • (2003) Ann. Statist. , vol.31 , pp. 225-251
    • Baraud, Y.1    Huet, S.2    Laurent, B.3
  • 4
    • 85015107636 scopus 로고    scopus 로고
    • Variable selection for highdimensional models: Partially faithful distributions and the PC-simple algorithm
    • To appear
    • BÜHLMANN, P., KALISCH, M. and MAATHUIS, M. H. (2009). Variable selection for highdimensional models: Partially faithful distributions and the PC-simple algorithm. Biometrika. To appear.
    • (2009) Biometrika
    • Bühlmann, P.1    Kalisch, M.2    Maathuis, M.H.3
  • 5
    • 34548275795 scopus 로고    scopus 로고
    • The Dantzig selector: Statistical estimation when p is much larger than n
    • CANDÈS, E. and TAO, T. (2007). The Dantzig selector: Statistical estimation when p is much larger than n. Ann. Statist. 35 2313-2351.
    • (2007) Ann. Statist. , vol.35 , pp. 2313-2351
    • Candès, E.1    Tao, T.2
  • 8
    • 49449118676 scopus 로고    scopus 로고
    • Multiple testing and error control in Gaussian graphical model selection
    • DRTON, M. and PERLMAN, M. (2007).Multiple testing and error control in Gaussian graphical model selection. Statist. Sci. 22 430-449.
    • (2007) Statist. Sci. , vol.22 , pp. 430-449
    • Drton, M.1    Perlman, M.2
  • 10
    • 85017322137 scopus 로고    scopus 로고
    • Estimation of Gaussian graphs by model selection
    • GIRAUD, C. (2008). Estimation of Gaussian graphs by model selection. Electron. J. Stat. 2 542-563.
    • (2008) Electron. J. Stat. , vol.2 , pp. 542-563
    • Giraud, C.1
  • 11
    • 33644986127 scopus 로고    scopus 로고
    • Covariance matrix selection and estimation via penalised normal likehood
    • HUANG, J., LIU, N., POURAHMADI, M. and LIU, L. (2006). Covariance matrix selection and estimation via penalised normal likehood. Biometrika 93 85-98.
    • (2006) Biometrika , vol.93 , pp. 85-98
    • Huang, J.1    Liu, N.2    Pourahmadi, M.3    Liu, L.4
  • 12
    • 0003058689 scopus 로고
    • Asymptotically minimax hypothesis testing for nonparametric alternatives i
    • INGSTER, Y. I. (1993). Asymptotically minimax hypothesis testing for nonparametric alternatives I. Math. Methods Statist. 2 85-114.
    • (1993) Math. Methods Statist. , vol.2 , pp. 85-114
    • Ingster, Y.I.1
  • 13
    • 0000484198 scopus 로고
    • Asymptotically minimax hypothesis testing for nonparametric alternatives II
    • INGSTER, Y. I. (1993). Asymptotically minimax hypothesis testing for nonparametric alternatives II. Math. Methods Statist. 3 171-189.
    • (1993) Math. Methods Statist. , vol.3 , pp. 171-189
    • Ingster, Y.I.1
  • 14
    • 0001047522 scopus 로고
    • Asymptotically minimax hypothesis testing for nonparametric alternatives III
    • INGSTER, Y. I. (1993). Asymptotically minimax hypothesis testing for nonparametric alternatives III. Math. Methods Statist. 4 249-268.
    • (1993) Math. Methods Statist. , vol.4 , pp. 249-268
    • Ingster, Y.I.1
  • 15
    • 0034570871 scopus 로고    scopus 로고
    • Correspondence analysis of genes and tissue types and finding genetic links from microarray data
    • KISHINO, H. and WADDELL, P. (2000). Correspondence analysis of genes and tissue types and finding genetic links from microarray data. Genome Informatics 11 83-95.
    • (2000) Genome Informatics , vol.11 , pp. 83-95
    • Kishino, H.1    Waddell, P.2
  • 16
    • 0034287154 scopus 로고    scopus 로고
    • Adaptive estimation of a quadratic function by model selection
    • LAURENT, B. andMASSART, P. (2000). Adaptive estimation of a quadratic function by model selection. Ann. Statist. 28 1302-1338.
    • (2000) Ann. Statist. , vol.28 , pp. 1302-1338
    • Laurent, B.1    Massart, P.2
  • 19
    • 33747163541 scopus 로고    scopus 로고
    • High-dimensional graphs and variable selection with the Lasso
    • MEINSHAUSEN, N. and BÜHLMANN, P. (2006). High-dimensional graphs and variable selection with the Lasso. Ann. Statist. 34 1436-1462.
    • (2006) Ann. Statist. , vol.34 , pp. 1436-1462
    • Meinshausen, N.1    Bühlmann, P.2
  • 21
    • 15944364151 scopus 로고    scopus 로고
    • An empirical Bayes approach to inferring large-scale gene association network
    • SCHÄFER, J. and STRIMMER, K. (2005). An empirical Bayes approach to inferring large-scale gene association network. Bioinformatics 21 754-764.
    • (2005) Bioinformatics , vol.21 , pp. 754-764
    • Schäfer, J.1    Strimmer, K.2
  • 22
    • 0030342998 scopus 로고    scopus 로고
    • Adaptative hypothesis testing using wavelets
    • SPOKOINY, V. G. (1996). Adaptative hypothesis testing using wavelets. Ann. Statist. 24 2477-2498.
    • (1996) Ann. Statist. , vol.24 , pp. 2477-2498
    • Spokoiny, V.G.1
  • 25
    • 30044444291 scopus 로고    scopus 로고
    • Low-order conditional independence graphs for inferring genetic networks
    • Art. 1(electronic)
    • WILLE, A. and BÜHLMANN, P. (2006). Low-order conditional independence graphs for inferring genetic networks. Stat. Appl. Genet. Mol. Biol. 5 Art. 1 (electronic).
    • (2006) Stat. Appl. Genet. Mol. Biol. , vol.5
    • Wille, A.1    Bühlmann, P.2
  • 27
    • 33947115409 scopus 로고    scopus 로고
    • Model selection and estimation in the Gaussian graphical model
    • YUAN, M. and LIN, Y. (2007). Model selection and estimation in the Gaussian graphical model. Biometrika 94 19-35.
    • (2007) Biometrika , vol.94 , pp. 19-35
    • Yuan, M.1    Lin, Y.2
  • 28
    • 50949096321 scopus 로고    scopus 로고
    • The sparsity and bias of the LASSO selection in highdimensional linear regression
    • ZHANG, C.-H. and HUANG, J. (2008). The sparsity and bias of the LASSO selection in highdimensional linear regression. Ann. Statist. 36 1567-1594.
    • (2008) Ann. Statist. , vol.36 , pp. 1567-1594
    • Zhang, C.-H.1    Huang, J.2
  • 29
    • 33845263263 scopus 로고    scopus 로고
    • On model selection consistency of Lasso
    • ZHAO, P. and YU, B. (2006). On model selection consistency of Lasso. J. Mach. Learn. Res. 7 2541-2563.
    • (2006) J. Mach. Learn. Res. , vol.7 , pp. 2541-2563
    • Zhao, P.1    Yu, B.2
  • 30
    • 16244401458 scopus 로고    scopus 로고
    • Regularization and variable selection via the elastic net
    • ZOU, H. andHASTIE, T. (2005). Regularization and variable selection via the Elastic Net. J. R. Stat. Soc. Ser. B. Stat. Methodol. 67 301-320.
    • (2005) J. R. Stat. Soc. Ser. B. Stat. Methodol. , vol.67 , pp. 301-320
    • Zou, H.1    Hastie, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.