R Package: cvplogistic. Penalized Logistic Regression Model using Majorization Minimization by Coordinate Descent (MMCD) Algorithm. The package uses majorization minimization by coordinate descent (MMCD) algorithm to compute the solution surface for concave penalized logistic regression model. The SCAD and MCP (default) are two concave penalties considered in this implementation. For the MCP penalty, the package also provides the local linear approximation by coordinate descant (LLA-CD) and adaptive rescaling algorithms for computing the solutions. The package also provides a Lasso-concave hybrid penalty for fast variable selection. The hybrid penalty applies the concave penalty only to the variables selected by the Lasso. For all the implemented methods, the solution surface is computed along kappa, which is a more smooth fit for the logistic model. Tuning parameter selection method by k-fold cross-validated area under ROC curve (CV-AUC) is implemented as well.
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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Liu, Wenchen; Tang, Yincai; Wu, Xianyi: Separating variables to accelerate non-convex regularized optimization (2020)
- Piotr Pokarowski, Wojciech Rejchel, Agnieszka Soltys, Michal Frej, Jan Mielniczuk: Improving Lasso for model selection and prediction (2019) arXiv
- Mkhadri, Abdallah; Ouhourane, Mohamed; Oualkacha, Karim: A coordinate descent algorithm for computing penalized smooth quantile regression (2017)
- Jiang, Dingfeng; Huang, Jian: Majorization minimization by coordinate descent for concave penalized generalized linear models (2014)