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In recent years, there has been considerable theoretical development regarding variable selection consistency of penalized regression techniques, such as the lasso.
Ridge regression and james-stein estimation: review and comments
Draper, N. R. and Van Nostrand, R. C. (1979) · 1979
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Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · 1996
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Theory of Point Estimation
Lehmann, E. and Casella, G. (1998) · 1998
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When should epidemiologic regressions use random coefficients?
Greenland, S. (2000) · 2000
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Asymptotics for lasso-type estimators
Knight, K. and Fu, W. (2000) · 2000
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Variable selection via nonconcave penalized likelihood and its oracle properties
Fan, J. and Li, R. (2001) · 2001
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Penalized spline estimation for partially linear single-index models
Yu, Y. and Ruppert, D. (2002) · 2002
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Semiparametric Regression
Ruppert, D., Wand, M. P., and Carroll, R. J. (2003) · 2003
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Least angle regression
Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. (2004) · 2004
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Model selection and inference: Facts and fiction
Leeb, H. and Pötscher, B. (2005) · 2005
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Regularization and variable selection via the elastic net
Zou, H. and Hastie, T. (2005) · 2005
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On model selection consistency of lasso
Zhao, P. and Yu, B. (2006) · 2006
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Model selection and estimation in regression with grouped variables
Yuan, M. and Lin, Y. (2007) · 2007
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Bolasso: Model consistent lasso estimation through the bootstrap
Bach, F. (2008) · 2008
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One-step sparse estimates in nonconcave penalized likelihood models
Zou, H. and Li, R. (2008) · 2008
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Hybrid and size-corrected subsampling methods
Andrews, D. W. and Guggenberger, P. (2009) · 2009
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Adding spatially-correlated errors can mess up the fixed effect you love
Hodges, J. S. and Reich, B. J. (2010) · 2010
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Stability selection
Meinshausen, N. and Bühlmann, P. (2010) · 2010
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Variance estimation using refitted cross-validation in ultrahigh dimensional regression
Fan, J., Guo, S., and Hao, N. (2012) · 2012
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Standardization and the group lasso penalty
Simon, N. and Tibshirani, R. (2012) · 2012
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Degrees of freedom in lasso problems
Tibshirani, R. J. and Taylor, J. (2012) · 2012
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Valid post-selection inference
Berk, R., Brown, L., Buja, A., Zhang, K., Zhao, L., et al. (2013) · 2013
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Confidence intervals and hypothesis testing for high-dimensional regression
Javanmard, A. and Montanari, A. (2013) · 2013
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Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection
Breheny, P. and Huang, J. (2011) · 2011
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Statistics for high-dimensional data: methods, theory and applications
Bühlmann, P. and Van De Geer, S. (2011) · 2011
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Bootstrapping lasso estimators
Chatterjee, A. and Lahiri, S. (2011) · 2011
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SparseNet: Coordinate descent with nonconvex penalties
Mazumder, R., Friedman, J. H., and Hastie, T. (2011) · 2011
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Confidence intervals for low-dimensional parameters with high-dimensional data
Zhang, C.-H. and Zhang, S. (2011) · 2011
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Lee, J. D., Sun, D. L., Sun, Y., and Taylor, J. E. (2013) · 2013
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A significance test for the lasso
Lockhart, R., Taylor, J., Tibshirani, R., and Tibshirani, R. (2013) · 2013
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The lasso problem and uniqueness
Tibshirani, R. J. (2013) · 2013
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On asymptotically optimal confidence regions and tests for high-dimensional models
van de Geer, S., Bühlmann, P., and Ritov, Y. (2013) · 2013
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Post-selection adaptive inference for least angle regression and the lasso
Taylor, J., Lockhart, R., Tibshirani, R. J., and Tibshirani, R. (2014) · 2014
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On the distribution of penalized maximum likelihood estimators: The lasso, scad, and thresholding
Pötscher, B. M. and Leeb, H. (2009) · 2082
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