References in zbMATH (referenced in 21 articles )

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  1. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  2. Pilanci, Mert; Wainwright, Martin J.: Newton sketch: a near linear-time optimization algorithm with linear-quadratic convergence (2017)
  3. Beinrucker, Andre; Dogan, Ürün; Blanchard, Gilles: Extensions of stability selection using subsamples of observations and covariates (2016)
  4. Biau, Gérard; Fischer, Aurélie; Guedj, Benjamin; Malley, James D.: COBRA: a combined regression strategy (2016)
  5. Fallahpour, Saber; Ejaz Ahmed, S.: Shrinkage estimation and variable selection in multiple regression models with random coefficient autoregressive errors (2014)
  6. Alfons, Andreas; Croux, Christophe; Gelper, Sarah: Sparse least trimmed squares regression for analyzing high-dimensional large data sets (2013)
  7. Flynn, Cheryl J.; Hurvich, Clifford M.; Simonoff, Jeffrey S.: Efficiency for regularization parameter selection in penalized likelihood estimation of misspecified models (2013)
  8. Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
  9. Nagarajan, Radhakrishnan; Scutari, Marco; Lèbre, Sophie: Bayesian networks in R. With applications in systems biology (2013)
  10. Eklund, Martin; Zwanzig, Silvelyn: SimSel: a new simulation method for variable selection (2012)
  11. Karabatsos, George; Walker, Stephen G.: Adaptive-modal Bayesian nonparametric regression (2012)
  12. Panaretos, Victor M.; Konis, Kjell: Sparse approximations of protein structure from noisy random projections (2011)
  13. Wand, M.P.; Ormerod, J.T.: Penalized wavelets: embedding wavelets into semiparametric regression (2011)
  14. Schifano, Elizabeth D.; Strawderman, Robert L.; Wells, Martin T.: Majorization-minimization algorithms for nonsmoothly penalized objective functions (2010)
  15. Bühlmann, Peter; Hothorn, Torsten: Boosting algorithms: regularization, prediction and model fitting (2007)
  16. Candès, Emmanuel; Tao, Terence: The Dantzig selector: statistical estimation when $p$ is much larger than $n$. (With discussions and rejoinder). (2007)
  17. Donoho, David L.: For most large underdetermined systems of linear equations the minimal $\ell_1$-norm solution is also the sparsest solution (2006)
  18. Meinshausen, Nicolai; Bühlmann, Peter: High-dimensional graphs and variable selection with the Lasso (2006)
  19. Yuan, Ming; Lin, Yi: Model selection and estimation in regression with grouped variables (2006)
  20. Zou, Hui: The adaptive lasso and its oracle properties (2006)

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