glmnet

R package glmnet: Lasso and elastic-net regularized generalized linear models. Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, poisson regression and the Cox model. Two recent additions are the multiresponse gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a pathwise fashion, as described in the paper listed below.


References in zbMATH (referenced in 206 articles )

Showing results 1 to 20 of 206.
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  1. Aravkin, Aleksandr; Burke, James V.; Ljung, Lennart; Lozano, Aurelie; Pillonetto, Gianluigi: Generalized Kalman smoothing: modeling and algorithms (2017)
  2. Calafiore, Giuseppe C.; Novara, Carlo; Taragna, Michele: Leading impulse response identification via the elastic net criterion (2017)
  3. Chiquet, Julien; Mary-Huard, Tristan; Robin, Stéphane: Structured regularization for conditional Gaussian graphical models (2017)
  4. Chow, Yat Tin; Wu, Tianyu; Yin, Wotao: Cyclic coordinate-update algorithms for fixed-point problems: analysis and applications (2017)
  5. Fu, Zhixuan; Parikh, Chirag R.; Zhou, Bingqing: Penalized variable selection in competing risks regression (2017)
  6. Giurcanu, Mihai C.: Oracle M-estimation for time series models (2017)
  7. Groll, Andreas; Tutz, Gerhard: Variable selection in discrete survival models including heterogeneity (2017)
  8. Jhong, Jae-Hwan; Koo, Ja-Yong; Lee, Seong-Whan: Penalized B-spline estimator for regression functions using total variation penalty (2017)
  9. Johnstone, Patrick R.; Moulin, Pierre: Local and global convergence of a general inertial proximal splitting scheme for minimizing composite functions (2017)
  10. Maciak, Matúš: Testing shape constraints in Lasso regularized joinpoint regression (2017)
  11. Moradi Rekabdarkolaee, Hossein; Wang, Qin: Variable selection through adaptive MAVE (2017)
  12. Simon Mak, C. F. Jeff Wu: cmenet: a new method for bi-level variable selection of conditional main effects (2017) arXiv
  13. Ternès, Nils; Rotolo, Federico; Heinze, Georg; Michiels, Stefan: Identification of biomarker-by-treatment interactions in randomized clinical trials with survival outcomes and high-dimensional spaces (2017)
  14. Yamamoto, Michio; Hwang, Heungsun: Dimension-reduced clustering of functional data via subspace separation (2017)
  15. Zhang, Haixiang; Zheng, Yinan; Yoon, Grace; Zhang, Zhou; Gao, Tao; Joyce, Brian; Zhang, Wei; Schwartz, Joel; Vokonas, Pantel; Colicino, Elena; Baccarelli, Andrea; Hou, Lifang; Liu, Lei: Regularized estimation in sparse high-dimensional multivariate regression, with application to a DNA methylation study (2017)
  16. Angelopoulos, Nicos; Abdallah, Samer; Giamas, Georgios: Advances in integrative statistics for logic programming (2016)
  17. Beinrucker, Andre; Dogan, Ürün; Blanchard, Gilles: Extensions of stability selection using subsamples of observations and covariates (2016)
  18. Blum, Yuna; Houée-Bigot, Magalie; Causeur, David: Sparse factor model for co-expression networks with an application using prior biological knowledge (2016)
  19. Fitzpatrick, Trevor; Mues, Christophe: An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market (2016)
  20. Fountoulakis, Kimon; Gondzio, Jacek: Performance of first- and second-order methods for $\ell_1$-regularized least squares problems (2016)

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