Variable Selection via A Combination of the L0 and L1 Penalties

By Yufeng Liu, Yichao Wu. 2007

98 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Li Wang
2. Yufeng Liu
3. Yichao Wu
4. Shujie Ma
5. Jianhua Z. Huang
6. Haipeng Shen
7. Qiongxia Song
8. Lijian Yang

In Statistics and Probability

1. Shujie Ma
2. Li Wang
3. Qiongxia Song
4. Yufeng Liu
5. Yichao Wu
6. Jianhua Z. Huang
7. Haipeng Shen
8. Lijian Yang

Citations in the corpus

• Sparse principal component analysis via regularized low rank matrix approximation (2008, methodology): “In addition to the soft/hard thresholding and SCAD penalties that we have considered, one can apply the Bridge penalty (Frank and Friedman, 1993) or the hybrid penalty that combines the L 0 and L 1 penalties (Liu and Wu, 2007).”
• Simultaneous variable selection and estimation in semiparametric modeling of longitudinal/clustered data (2013, methodology): “There is an extensive literature on variable selection via various approaches, for example, the classical information criteria such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC) in Yang [40], the least absolute shrinkage and selection operator (LASSO) proposed in”
• VARIABLE SELECTION IN QUANTILE REGRESSION (2009, methodology): “…in best subsect selection (Breiman (1996)); the L 1 penalty (LASSO) (Tibshirani (1996)); the L 2 penalty used in ridge regression (Hoerl and Kennard (1988)); the combination of the L 0 and L 1 penalties (Liu and Wu (2007)); the L q (q ≥ 0) penalties in bridge regression (Frank and Friedman (1993)).”