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 422 articles , 1 standard article )

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  1. Simpson, Shawn E.; Madigan, David; Zorych, Ivan; Schuemie, Martijn J.; Ryan, Patrick B.; Suchard, Marc A.: Multiple self-controlled case series for large-scale longitudinal observational databases (2013)
  2. Sivia Tian, Tian; James, Gareth M.: Interpretable dimension reduction for classifying functional data (2013)
  3. Taylor, Jonathan: The geometry of least squares in the 21st century (2013)
  4. Ueki, Masao; Kawasaki, Yoshinori: Multiple choice from competing regression models under multicollinearity based on standardized update (2013)
  5. Wang, Qin; Wu, Rongning: Shrinkage estimation of partially linear single-index models (2013)
  6. Yang, Yi; Zou, Hui: A cocktail algorithm for solving the elastic net penalized Cox’s regression in high dimensions (2013)
  7. Yao, Weixin; Wang, Qin: Robust variable selection through MAVE (2013)
  8. Zahid, Faisal Maqbool; Tutz, Gerhard: Multinomial logit models with implicit variable selection (2013)
  9. Zahid, Faisal Maqbool; Tutz, Gerhard: Ridge estimation for multinomial logit models with symmetric side constraints (2013)
  10. Daye, Z. John; Chen, Jinbo; Li, Hongzhe: High-dimensional heteroscedastic regression with an application to eQTL data analysis (2012)
  11. Hossain, Shakhawat; Ahmed, Ejaz: Shrinkage and penalty estimators of a Poisson regression model (2012)
  12. Ji, Pengsheng; Jin, Jiashun: UPS delivers optimal phase diagram in high-dimensional variable selection (2012)
  13. Karabatsos, George; Walker, Stephen G.: Adaptive-modal Bayesian nonparametric regression (2012)
  14. Kiiveri, Harri; de Hoog, Frank: Fitting very large sparse Gaussian graphical models (2012)
  15. Lecué, Guillaume; Mitchell, Charles: Oracle inequalities for cross-validation type procedures (2012)
  16. Li, Ran; Wu, Baolin: Sparse regularized discriminant analysis with application to microarrays (2012)
  17. Mai, Qing; Zou, Hui; Yuan, Ming: A direct approach to sparse discriminant analysis in ultra-high dimensions (2012)
  18. Michel, Vincent; Gramfort, Alexandre; Varoquaux, Gaël; Eger, Evelyn; Keribin, Christine; Thirion, Bertrand: A supervised clustering approach for fMRI-based inference of brain states (2012)
  19. Raheem, S. M. Enayetur; Ahmed, S. Ejaz; Doksum, Kjell A.: Absolute penalty and shrinkage estimation in partially linear models (2012)
  20. Rigollet, Philippe; Tsybakov, Alexandre B.: Sparse estimation by exponential weighting (2012)

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