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Regularized linear regression under the $\ell_1$ penalty, such as the Lasso, has been shown to be effective in variable selection and sparse modeling.
Frank, I. and Friedman, J. (1993), “A statistical view of some chemometrics regression tools (with discussion),” Technometrics
1993
Earlier work this paper cites.
Green, P. (1995), “Reversible jump Markov chain Monte Carlo computation and Bayesian model determination,” Biometrika
1995
Earlier work this paper cites.
Tibshirani, R. (1996), “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society. Series B
1996
Earlier work this paper cites.
Chen, S., Donoho, D. L., and Saunders, M. (1999), “Atomic decomposition by basis pursuit,” SIAM Journal of Scientific Computing
1999
Earlier work this paper cites.
Knight, K. and Fu, W. (2000), “Asymptotics for Lasso-Type estimators,” The Annals of Statistics
2000
Earlier work this paper cites.
Osborne, M., Presnell, B., and Turlach, B. (2000), “A new approach to variable selection in least squares problems,” IMA Journal of Numerical Analysis
2000
Earlier work this paper cites.
Fan, J. and Li, R. (2001), “Variable Selection via Nonconcave Penalized Likelihood and Its Oracle Properties,” Journal of the American Statistical Association
2001
Earlier work this paper cites.
Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. (2004), “Least Angle Regression,” The Annals of Statistics
2004
Earlier work this paper cites.
Meinshausen, N. and Bühlmann, P. (2006), “High-Dimensional Graphs and Variable Selection with the Lasso,” The Annals of Statistics
2006
Earlier work this paper cites.
Yuan, M. and Lin, Y. (2006), “Model selection and estimation in regression with grouped variables,” Journal of the Royal Statistical Society. Series B
2006
Earlier work this paper cites.
Zhao, P. and Yu, B. (2006), “On model selection consistency of Lasso,” Journal of Machine Learning Research
2006
Earlier work this paper cites.
Zou, H. (2006), “The Adaptive Lasso and Its Oracle Properties,” Journal of the American Statistical Association
2006
Earlier work this paper cites.
Friedman, J., Hastie, T., Höfling, H., and Tibshirani, R. (2007), “Pathwise Coordinate Optimization,” The Annals of Applied Statistics
2007
Cited alongside, same era.
Bach, F. (2008), “Bolasso: model consistent lasso estimation through the bootstrap,” in Proceedings of the 25th International Conference on Machine Learning
2008
Cited alongside, same era.
Friedman, J., Hastie, T., and Tibshirani, R. (2008), “Sparse Inverse Covariance Estimation with the Graphical Lasso,” Biostatistics
2008
Cited alongside, same era.
Wu, T. and Lange, K. (2008), “Coordinate Descent Procedures for Lasso Penalized Regression,” The Annals of Applied Statistics
2008
Cited alongside, same era.
Zhang, C. and Huang, J. (2008), “The sparsity and bias of the LASSO selection in high-dimensional linear regression,” The Annals of Statistics
2008
Cited alongside, same era.
— (2010), “Confidence sets based on penalized maximum likelihood estimators in Gaussian regression,” Electronic Journal of Statistics
2010
Later among the works it cites.
Zhang, C. (2010), “Nearly unbiased variable selection under minimax concave penalty,” The Annals of Statistics
2010
Later among the works it cites.
— (2011), “Boostrapping Lasso estimators,” Journal of the American Statistical Association
2011
Later among the works it cites.
Lounici, K., Pontil, M., van de Geer, S., and Tsybakov, A. (2011), “Oracle inequalities and optimal inference under group sparsity,” Annals of Statistics
2011
Later among the works it cites.
Minnier, J., Tian, L., and Cai, T. (2011), “A perturbation method for inference on regularized regression estimates,” Journal of the American Statistical Association
2011
Later among the works it cites.
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2008
Cited alongside, same era.
Bickel, P., Ritov, Y., and Tsybakov, A. (2009), “Simultaneous analysis of Lasso and Dantzig selector,” Annals of Statistics
2009
Cited alongside, same era.
Meinshausen, N. and Yu, B. (2009), “Lasso-type recovery of sparse representations for high-dimensional data,” The Annals of Statistics
2009
Cited alongside, same era.
Pötscher, B. M. and Schneider, U. (2009), “On the distribution of the adaptive LASSO estimator,” Journal of Statistical Planning and Inference
2009
Cited alongside, same era.
Wainwright, M. (2009), “Sharp thresholds for high-dimensional and noisy sparsity recovery using ℓ 1 \ell_{1} -constrained quadratic programming (Lasso),” IEEE Transactions on Information Theory
2009
Cited alongside, same era.
Chatterjee, A. and Lahiri, S. (2010), “Asymptotic properties of the residual bootstrap for Lasso estimators,” Proceedings of the American Mathematical Society
2010
Cited alongside, same era.
Meinshausen, N. and Bühlmann, P. (2010), “Stability selection (with discussion),” Journal of the Royal Statistical Society series B
2010
Cited alongside, same era.
Sun, T. and Zhang, C. (2012), “Scaled sparse linear regression,” Biometrika
2012
Later among the works it cites.
— (2013), “Rates of convergence of the adaptive Lasso estimators to the oracle distribution and higher order refinements by the bootstrap,” The Annals of Statistics
2013
Later among the works it cites.
— (2013), “The lasso problem and uniqueness,” Electronic Journal of Statistics
2013
Later among the works it cites.
van de Geer, S., Bühlmann, P., Ritov, Y., and Dezeure, R. (2013), “Confidence regions and tests for high-dimensional models,” arXiv
2013
Later among the works it cites.
Lockhart, R., Taylor, J., Tibshirani, R., and Tibshirani, R. (2014), “A significance test for the lasso,” Annals of Statistics
2014
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Zhang, C. and Zhang, S. (2014), “Confidence intervals for low-dimensional parameters in high-dimensional linear models,” Journal of the Royal Statistical Society. Series B
2014
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