A Study of Error Variance Estimation in Lasso Regression
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[1] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[2] Jianqing Fan,et al. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .
[3] Y. Ritov,et al. Persistence in high-dimensional linear predictor selection and the virtue of overparametrization , 2004 .
[4] L. Wasserman,et al. HIGH DIMENSIONAL VARIABLE SELECTION. , 2007, Annals of statistics.
[5] Cun-Hui Zhang,et al. Comments on: ℓ1-penalization for mixture regression models , 2010 .
[6] S. Geer,et al. ℓ1-penalization for mixture regression models , 2010, 1202.6046.
[7] Jianqing Fan,et al. Comments on: ℓ1-penalization for mixture regression models , 2010 .
[8] Cun-Hui Zhang,et al. Scaled sparse linear regression , 2011, 1104.4595.
[9] Jianqing Fan,et al. Variance estimation using refitted cross‐validation in ultrahigh dimensional regression , 2010, Journal of the Royal Statistical Society. Series B, Statistical methodology.
[10] Cun-Hui Zhang,et al. Sparse matrix inversion with scaled Lasso , 2012, J. Mach. Learn. Res..
[11] Daniel J. McDonald,et al. The lasso, persistence, and cross-validation , 2013, ICML.
[12] Adel Javanmard,et al. Confidence intervals and hypothesis testing for high-dimensional regression , 2013, J. Mach. Learn. Res..
[13] R. Tibshirani,et al. A SIGNIFICANCE TEST FOR THE LASSO. , 2013, Annals of statistics.
[14] Lee H. Dicker,et al. Variance estimation in high-dimensional linear models , 2014 .