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Volumn 99, Issue 1, 2012, Pages 15-28

Factor profiled sure independence screening

Author keywords

Factor profiled sure independence screening; Factor profiling; Maximum eigenvalue ratio criterion; Screening consistency; Sure independence screening

Indexed keywords


EID: 84863252835     PISSN: 00063444     EISSN: 14643510     Source Type: Journal    
DOI: 10.1093/biomet/asr074     Document Type: Article
Times cited : (65)

References (40)
  • 1
    • 0000501656 scopus 로고
    • Information theory and an extension of the maximum likelihood principle
    • Ed. B. N. Petrov and F. Csaki Budapest: Akademia Kiado
    • AKAIKE, H. (1973). Information theory and an extension of the maximum likelihood principle. In 2nd Int. Symp. Info. Theory, Ed. B. N. Petrov and F. Csaki, pp. 267-81. Budapest: Akademia Kiado.
    • (1973) 2nd Int. Symp. Info. Theory , pp. 267-281
    • Akaike, H.1
  • 2
    • 41549106844 scopus 로고    scopus 로고
    • Regularized estimation of large covariance matrices
    • BICKEL, P. J. & LEVINA, E. (2008). Regularized estimation of large covariance matrices. Ann. Statist. 36, 199-277.
    • (2008) Ann. Statist. , vol.36 , pp. 199-277
    • Bickel, P.J.1    Levina, E.2
  • 3
    • 84874257732 scopus 로고
    • Better subset selection using nonnegative garrote
    • BREIMAN, L. (1995). Better subset selection using nonnegative garrote. Technometrics 37, 373-84.
    • (1995) Technometrics , vol.37 , pp. 373-384
    • Breiman, L.1
  • 4
    • 0030344230 scopus 로고    scopus 로고
    • Heuristics of instability and stabilization in model selection
    • BREIMAN, L. (1996). Heuristics of instability and stabilization in model selection. Ann. Statist. 24, 2350-83.
    • (1996) Ann. Statist. , vol.24 , pp. 2350-2383
    • Breiman, L.1
  • 5
    • 50949108781 scopus 로고    scopus 로고
    • Extended Bayesian information criterion for model selection with large model spaces
    • CHEN, J. & CHEN, Z. (2008). Extended Bayesian information criterion for model selection with large model spaces. Biometrika 95, 759-71.
    • (2008) Biometrika , vol.95 , pp. 759-771
    • Chen, J.1    Chen, Z.2
  • 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. (Pubitemid 33695585)
    • (2001) Journal of the American Statistical Association , vol.96 , Issue.456 , pp. 1348-1360
    • Fan, J.1    Li, R.2
  • 8
    • 53849086824 scopus 로고    scopus 로고
    • Sure independence screening for ultra-high dimensional feature space (with discussion)
    • FAN, J. & LV, J. (2008). Sure independence screening for ultra-high dimensional feature space (with discussion). J. R. Statist. Soc. B 70, 849-911.
    • (2008) J. R. Statist. Soc. B , vol.70 , pp. 849-911
    • Fan, J.1    J, L.V.2
  • 9
    • 24344502730 scopus 로고    scopus 로고
    • Nonconcave penalized likelihood with a diverging number of parameters
    • DOI 10.1214/009053604000000256
    • FAN, J. & PENG, H. (2004). On non-concave penalized likelihood with diverging number of parameters. Ann. Statist. 32, 928-61. (Pubitemid 41250289)
    • (2004) Annals of Statistics , vol.32 , Issue.3 , pp. 928-961
    • Fan, J.1    Peng, H.2
  • 10
    • 55349144848 scopus 로고    scopus 로고
    • High dimensional covariance matrix estimation using a factor model
    • FAN, J., FAN, Y. & LV, J. (2008). High dimensional covariance matrix estimation using a factor model. J. Economet. 147, 186-97.
    • (2008) J. Economet. , vol.147 , pp. 186-197
    • Fan, J.1    Fan, Y.2    J, L.V.3
  • 11
    • 0032361278 scopus 로고    scopus 로고
    • Penalized regression: The bridge versus the lasso
    • FU, W. J. (1998). Penalized regression: the bridge versus the lasso. J. Comp. Graph. Statist. 7, 397-416.
    • (1998) J. Comp. Graph. Statist. , vol.7 , pp. 397-416
    • J, F.U.W.1
  • 13
    • 49949115667 scopus 로고    scopus 로고
    • Asymptotic properties of bridge estimators in sparse high-dimensional regression models
    • HUANG, J., HOROWITZ, J. & MA, S. (2007). Asymptotic properties of bridge estimators in sparse high-dimensional regression models. Ann. Statist. 36, 587-613.
    • (2007) Ann. Statist. , vol.36 , pp. 587-613
    • Huang, J.1    Horowitz, J.2    Ma, S.3
  • 15
    • 0034287156 scopus 로고    scopus 로고
    • Asymptotics for lasso-type estimators
    • DOI 10.1214/aos/1015957397
    • KNIGHT, K. & FU, W. (2000). Asymptotics for lasso-type estimators. Ann. Statist. 28, 1356-78. (Pubitemid 33244917)
    • (2000) Annals of Statistics , vol.28 , Issue.5 , pp. 1356-1378
    • Knight, K.1    Fu, W.2
  • 16
    • 33846193774 scopus 로고    scopus 로고
    • A note on lasso and related procedures in model selection
    • LENG, C., LIN, Y. & WAHBA, G. (2006). A note on lasso and related procedures in model selection. Statist. Sinica 16, 1273-84.
    • (2006) Statist. Sinica , vol.16 , pp. 1273-1284
    • Leng, C.1    Lin, Y.2    Wahba, G.3
  • 17
    • 84945116550 scopus 로고
    • Sliced inverse regression for dimension reduction
    • LI, K.-C. (1991). Sliced inverse regression for dimension reduction. J. Am. Statist. Assoc. 86, 316-27.
    • (1991) J. Am. Statist. Assoc. , vol.86 , pp. 316-327
    • Li, K.-C.1
  • 18
    • 34548540582 scopus 로고    scopus 로고
    • Partial inverse regression
    • DOI 10.1093/biomet/asm043
    • LI, L., COOK, R. D. & TSAI, C. L. (2007). Partial inverse regression. Biometrika 94, 615-25. (Pubitemid 47384254)
    • (2007) Biometrika , vol.94 , Issue.3 , pp. 615-625
    • Li, L.1    Cook, R.D.2    Tsai, C.-L.3
  • 19
    • 73149086049 scopus 로고    scopus 로고
    • Contour projected dimension reduction
    • LUO, R.,WANG, H. & TSAI, C. L. (2009). Contour projected dimension reduction. Ann. Statist. 37, 3743-78.
    • (2009) Ann. Statist. , vol.37 , pp. 3743-3778
    • Luo, R.1    Wang, H.2    Tsai, C.L.3
  • 20
    • 44849137877 scopus 로고    scopus 로고
    • Modelling multiple time series via common factors
    • DOI 10.1093/biomet/asn009
    • PAN, J. & YAO, Q. (2008). Modelling multiple time series via common factors. Biometrika 95, 365-79. (Pubitemid 351797215)
    • (2008) Biometrika , vol.95 , Issue.2 , pp. 365-379
    • Pan, J.1    Yao, Q.2
  • 21
    • 0000120766 scopus 로고
    • Estimating the dimension of a model
    • SCHWARZ, G. (1978). Estimating the dimension of a model. Ann. Statist. 6, 461-4.
    • (1978) Ann. Statist. , vol.6 , pp. 461-464
    • Schwarz, G.1
  • 22
    • 0642336882 scopus 로고    scopus 로고
    • An asymptotic theory for linear model selection
    • SHAO, J. (1997). An asymptotic theory for linear model selection. Statist. Sinica 7, 221-64. (Pubitemid 127473663)
    • (1997) Statistica Sinica , vol.7 , Issue.2 , pp. 221-264
    • Shao, J.1
  • 23
    • 0036012998 scopus 로고    scopus 로고
    • Regression model selection-a residual likelihood approach
    • SHI, P. & TSAI, C. L. (2002). Regression model selection-a residual likelihood approach. J. R. Statist. Soc. B 64, 237-52.
    • (2002) J. R. Statist. Soc. B , vol.64 , pp. 237-252
    • Shi, P.1    Tsai, C.L.2
  • 24
    • 0001287271 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • TIBSHIRANI, R. J. (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.J.1
  • 25
    • 74049114100 scopus 로고    scopus 로고
    • Forward regression for ultra-high dimensional variable screening
    • WANG, H. (2009). Forward regression for ultra-high dimensional variable screening. J. Am. Statist. Assoc. 104, 1512-24.
    • (2009) J. Am. Statist. Assoc. , vol.104 , pp. 1512-1524
    • Wang, H.1
  • 26
    • 66549114275 scopus 로고    scopus 로고
    • Unified lasso estimation via least squares approximation
    • WANG, H. & LENG, C. (2007). Unified lasso estimation via least squares approximation. J. Am. Statist. Assoc. 101, 1418-29.
    • (2007) J. Am. Statist. Assoc. , vol.101 , pp. 1418-1429
    • Wang, H.1    Leng, C.2
  • 27
    • 49549101089 scopus 로고    scopus 로고
    • Sliced regression for dimension reduction
    • WANG, H. & XIA, Y. (2008). Sliced regression for dimension reduction. J. Am. Statist. Assoc. 103, 811-21.
    • (2008) J. Am. Statist. Assoc. , vol.103 , pp. 811-821
    • Wang, H.1    Xia, Y.2
  • 28
    • 34548536572 scopus 로고    scopus 로고
    • Tuning parameter selectors for the smoothly clipped absolute deviationmethod
    • WANG, H.,LI, R. & TSAI, C. L. (2007). Tuning parameter selectors for the smoothly clipped absolute deviationmethod. Biometrika 94, 553-8.
    • (2007) Biometrika , vol.94 , pp. 553-558
    • Wang, H.1    Li, R.2    Tsai, C.L.3
  • 29
    • 66849138434 scopus 로고    scopus 로고
    • Shrinkage tuning parameter selection with a diverging number of parameters
    • WANG, H., LI, B. & LENG, C. (2009). Shrinkage tuning parameter selection with a diverging number of parameters. J. R. Statist. Soc. B 71, 671-83.
    • (2009) J. R. Statist. Soc. B , vol.71 , pp. 671-683
    • Wang, H.1    B, L.I.2    Leng, C.3
  • 31
    • 49549110291 scopus 로고    scopus 로고
    • A constructive approach to the estimation of dimension reduction directions
    • XIA, Y. (2007). A constructive approach to the estimation of dimension reduction directions. Ann. Statist. 35, 2654-90.
    • (2007) Ann. Statist. , vol.35 , pp. 2654-2690
    • Xia, Y.1
  • 32
    • 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
  • 33
    • 50949096321 scopus 로고    scopus 로고
    • The sparsity and bias of the lasso selection in high-dimensional linear regression
    • ZHANG, C. H. & HUANG, J. (2008). The sparsity and bias of the lasso selection in high-dimensional linear regression. Ann. Statist. 36, 1567-94.
    • (2008) Ann. Statist. , vol.36 , pp. 1567-1594
    • Zhang, C.H.1    Huang, J.2
  • 34
    • 34548151636 scopus 로고    scopus 로고
    • Adaptive Lasso for Cox's proportional hazards model
    • DOI 10.1093/biomet/asm037
    • ZHANG, H. H. & LU, W. (2007). Adaptive lasso for Cox's proportional hazard model. Biometrika 94, 691-703. (Pubitemid 47384259)
    • (2007) Biometrika , vol.94 , Issue.3 , pp. 691-703
    • Zhang, H.H.1    Lu, W.2
  • 35
    • 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, 2541-67.
    • (2006) J.Mach. Learn. Res. , vol.7 , pp. 2541-2567
    • Zhao, P.1    B, Y.U.2
  • 36
    • 62849115699 scopus 로고    scopus 로고
    • On distribution-weighted partial least squares with diverging number of highly correlated predictors
    • ZHU, L. P. & ZHU, L. X. (2009). On distribution-weighted partial least squares with diverging number of highly correlated predictors. J. R. Statist. Soc. B 71, 525-48.
    • (2009) J. R. Statist. Soc. B , vol.71 , pp. 525-548
    • Zhu, L.P.1    Zhu, L.X.2
  • 37
    • 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
  • 39
    • 51049104549 scopus 로고    scopus 로고
    • One-step sparse estimates in nonconcave penalized likelihood models (with discussion
    • ZOU, H. & LI, R. (2008). One-step sparse estimates in nonconcave penalized likelihood models (with discussion). Ann. Statist. 36, 1509-33.
    • (2008) Ann. Statist. , vol.36 , pp. 1509-1533
    • Zou, H.1    R, L.I.2
  • 40
    • 62549126570 scopus 로고    scopus 로고
    • On the adaptive elastic-net with a diverging number of parameters
    • ZOU, H. & ZHANG, H. H. (2009). On the adaptive elastic-net with a diverging number of parameters. Ann. Statist. 37, 1733-51.
    • (2009) Ann. Statist. , vol.37 , pp. 1733-1751
    • Zou, H.1    Zhang, H.H.2


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