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Volumn 96, Issue 2, 2009, Pages 307-322

Hierarchically penalized Cox regression with grouped variables

Author keywords

Cox model; Group variable selection; Lasso; Microarray; Oracle property; Regularization

Indexed keywords


EID: 66249113503     PISSN: 00063444     EISSN: 14643510     Source Type: Journal    
DOI: 10.1093/biomet/asp016     Document Type: Article
Times cited : (62)

References (28)
  • 1
    • 0001646484 scopus 로고
    • Cox's regression model for counting processes: A large sample study
    • ANDERSEN, P. K. & GILL, R. D. (1982). Cox's regression model for counting processes: a large sample study. Ann. Statist. 10, 1100-20.
    • (1982) Ann. Statist , vol.10 , pp. 1100-1120
    • ANDERSEN, P.K.1    GILL, R.D.2
  • 2
    • 0442312210 scopus 로고    scopus 로고
    • ANTONIADIS, A. & AND FAN, J. (2001). Regularization of wavelet approximations (with discussions). J. Am. Statist. Assoc. 96, 939-67.
    • ANTONIADIS, A. & AND FAN, J. (2001). Regularization of wavelet approximations (with discussions). J. Am. Statist. Assoc. 96, 939-67.
  • 3
    • 84874257732 scopus 로고
    • Better subset regression using the non-negative garrote
    • BREIMAN, L. (1995). Better subset regression using the non-negative garrote. Technometrics 37, 373-84.
    • (1995) Technometrics , vol.37 , pp. 373-384
    • BREIMAN, L.1
  • 4
    • 0015980662 scopus 로고
    • Covariance analysis of censored survival data
    • BRESLOW, N. (1974). Covariance analysis of censored survival data. Biometrics 30, 89-99.
    • (1974) Biometrics , vol.30 , pp. 89-99
    • BRESLOW, N.1
  • 5
    • 33644553976 scopus 로고    scopus 로고
    • Discussion of "Regularization of wavelet approximations," by Antoniadis & Fan
    • CAI, T. (2001). Discussion of "Regularization of wavelet approximations," by Antoniadis & Fan. J. Am. Statist. Assoc. 96, 960-2.
    • (2001) J. Am. Statist. Assoc , vol.96 , pp. 960-962
    • CAI, T.1
  • 6
    • 0000336139 scopus 로고
    • Regression models and life-tables (with discussion)
    • COX, D. R. (1972). Regression models and life-tables (with discussion). J. R. Statist. Soc. B 34, 187-220.
    • (1972) J. R. Statist. Soc. B , vol.34 , pp. 187-220
    • COX, D.R.1
  • 7
    • 1542784498 scopus 로고    scopus 로고
    • Variable selection via nonconcave penalized likelihood and its oracle properties
    • FAN, J. & LI, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. J. Am. Statist. Assoc. 96, 1348-60.
    • (2001) J. Am. Statist. Assoc , vol.96 , pp. 1348-1360
    • FAN, J.1    LI, R.2
  • 8
    • 0036117466 scopus 로고    scopus 로고
    • Variable selection for Cox's proportional hazards model and frailty model
    • FAN, J. & LI, R. (2002). Variable selection for Cox's proportional hazards model and frailty model. Ann. Statist. 30, 74-99.
    • (2002) Ann. Statist , vol.30 , pp. 74-99
    • FAN, J.1    LI, R.2
  • 9
    • 84952149204 scopus 로고
    • A statistical view of some chemometrics regression tools (with discussion)
    • FRANK, I. E. & FRIEDMAN, J. H. (1993). A statistical view of some chemometrics regression tools (with discussion). Technometrics 35, 109-48.
    • (1993) Technometrics , vol.35 , pp. 109-148
    • FRANK, I.E.1    FRIEDMAN, J.H.2
  • 10
    • 21444446838 scopus 로고    scopus 로고
    • Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data
    • GUI, J. & LI, H. (2005). Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data. Bioinformatics 21, 3001-8.
    • (2005) Bioinformatics , vol.21 , pp. 3001-3008
    • GUI, J.1    LI, H.2
  • 11
    • 0033982936 scopus 로고    scopus 로고
    • KEGG: Kyoto encyclopedia of genes and genomes
    • KANEHISA, M. & GOTO, S. (2002). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27-30.
    • (2002) Nucleic Acids Res , vol.28 , pp. 27-30
    • KANEHISA, M.1    GOTO, S.2
  • 12
    • 37249033229 scopus 로고    scopus 로고
    • Group additive regression models for genomic data analysis
    • LUAN, Y. & LI, H. (2008). Group additive regression models for genomic data analysis. Biostatistics 9, 100-13.
    • (2008) Biostatistics , vol.9 , pp. 100-113
    • LUAN, Y.1    LI, H.2
  • 14
    • 34547849507 scopus 로고    scopus 로고
    • 1 -regularization path algorithm for generalized linear models
    • 1 -regularization path algorithm for generalized linear models. J. R. Statist. Soc. B 69, 659-77.
    • (2007) J. R. Statist. Soc. B , vol.69 , pp. 659-677
    • PARK, M.Y.1    HASTIE, T.2
  • 15
    • 0036489055 scopus 로고    scopus 로고
    • Adaptive model selection
    • SHEN, X. & YE, J. (2002). Adaptive model selection. J. Am. Statist. Assoc. 97, 210-21.
    • (2002) J. Am. Statist. Assoc , vol.97 , pp. 210-221
    • SHEN, X.1    YE, J.2
  • 16
    • 33144462268 scopus 로고    scopus 로고
    • SOTIRIOU, C, WIRAPATI, P., LOI, S., HARRIS, A., FOX, S., SMEDS, J., NORDGREN, H., FARMER, P., PRAZ, V., HAIBE-KAINS, B., DESMEDT, C., LARSIMONT, D., CARDOSO, F., PETERSE, H., NUYTEN, D., BUYSE, M., VAN DE VIJVER, M. J., BERGH, J., PICCART, M. & DELORENZI, M. (2006). Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J. Nat. Cancer Inst. 98, 262-72.
    • SOTIRIOU, C, WIRAPATI, P., LOI, S., HARRIS, A., FOX, S., SMEDS, J., NORDGREN, H., FARMER, P., PRAZ, V., HAIBE-KAINS, B., DESMEDT, C., LARSIMONT, D., CARDOSO, F., PETERSE, H., NUYTEN, D., BUYSE, M., VAN DE VIJVER, M. J., BERGH, J., PICCART, M. & DELORENZI, M. (2006). Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J. Nat. Cancer Inst. 98, 262-72.
  • 17
    • 0034069495 scopus 로고    scopus 로고
    • Gene ontology: Tool for the unification of biology
    • THE GENE ONTOLOGY CONSORTIUM
    • THE GENE ONTOLOGY CONSORTIUM (2000). Gene ontology: tool for the unification of biology. Nat. Genet. 25, 259.
    • (2000) Nat. Genet , vol.25 , pp. 259
  • 18
    • 0001287271 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • TIBSHIRANI, R. (1996). Regression shrinkage and selection via the lasso. J. R. Statist. Soc. B 58, 267-88.
    • (1996) J. R. Statist. Soc. B , vol.58 , pp. 267-288
    • TIBSHIRANI, R.1
  • 19
    • 0031015557 scopus 로고    scopus 로고
    • The lasso method for variable selection in the Cox model
    • TIBSHIRANI, R. (1997). The lasso method for variable selection in the Cox model. Statist. Med. 16, 385-95.
    • (1997) Statist. Med , vol.16 , pp. 385-395
    • TIBSHIRANI, R.1
  • 20
    • 33846190566 scopus 로고    scopus 로고
    • Regression coefficient and autoregressive order shrinkage and selection via the lasso
    • WANG, H., LI, G. & TSAI, C. L. (2007). Regression coefficient and autoregressive order shrinkage and selection via the lasso. J. R. Statist. Soc. B 69, 63-78.
    • (2007) J. R. Statist. Soc. B , vol.69 , pp. 63-78
    • WANG, H.1    LI, G.2    TSAI, C.L.3
  • 21
    • 34147120136 scopus 로고    scopus 로고
    • Nonparametric pathway-based regression models for analysis of genomic data
    • WEI, Z. & LI, H. (2007). Nonparametric pathway-based regression models for analysis of genomic data. Biostatistics 8, 265-84.
    • (2007) Biostatistics , vol.8 , pp. 265-284
    • WEI, Z.1    LI, H.2
  • 22
    • 33645035051 scopus 로고    scopus 로고
    • Model selection and estimation in regression with grouped variables
    • YUAN, M. & LIN, Y (2006). Model selection and estimation in regression with grouped variables. J. R. Statist. Soc. B 68, 49-67.
    • (2006) J. R. Statist. Soc. B , vol.68 , pp. 49-67
    • YUAN, M.1    LIN, Y.2
  • 23
    • 33847364905 scopus 로고    scopus 로고
    • On the nonnegative garrote estimator
    • YUAN, M. & LIN , Y (2007). On the nonnegative garrote estimator. J. R. Statist. Soc. B 69, 143-61.
    • (2007) J. R. Statist. Soc. B , vol.69 , pp. 143-161
    • YUAN, M.1    LIN, Y.2
  • 24
    • 47749133494 scopus 로고    scopus 로고
    • Z HANG, H. H., LIU, Y., W U, Y & ZHU , J. (2006). Variable selection for multicategory SVM via sup-norm regularization. Electron. J. Statist. 2, 149-67.
    • Z HANG, H. H., LIU, Y., W U, Y & ZHU , J. (2006). Variable selection for multicategory SVM via sup-norm regularization. Electron. J. Statist. 2, 149-67.
  • 25
    • 34548151636 scopus 로고    scopus 로고
    • Adaptive-lasso for Cox's proportional hazard model
    • ZHANG, H. H. & LU, W (2007). Adaptive-lasso for Cox's proportional hazard model. Biometrika 94, 691-703.
    • (2007) Biometrika , vol.94 , pp. 691-703
    • ZHANG, H.H.1    LU, W.2
  • 26
    • 33845263263 scopus 로고    scopus 로고
    • On model selection consistency of lasso
    • ZHAO, P. & YU, B. (2006). On model selection consistency of lasso. J. Mach. Learn. Res. 7, 2541-63.
    • (2006) J. Mach. Learn. Res , vol.7 , pp. 2541-2563
    • ZHAO, P.1    YU, B.2
  • 27
    • 33846114377 scopus 로고    scopus 로고
    • The adaptive lasso and its oracle properties
    • ZOU, H. (2006). The adaptive lasso and its oracle properties. J. Am. Statist. Assoc. 101, 1418-29.
    • (2006) J. Am. Statist. Assoc , vol.101 , pp. 1418-1429
    • ZOU, H.1
  • 28
    • 40249107663 scopus 로고    scopus 로고
    • A note on path-based variable selection in the penalized proportional hazards model
    • ZOU, H. (2008). A note on path-based variable selection in the penalized proportional hazards model. Biometrika 95, 241-7.
    • (2008) Biometrika , vol.95 , pp. 241-247
    • ZOU, H.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.