Name
Abstract | ||
|---|---|---|
Lasso is a celebrated method for variable selection in linear models, but it faces challenges when the variables are moderately or strongly correlated. This motivates alternative approaches such as using a non-convex penalty, adding a ridge regularization, or conducting a post-Lasso thresholding. In this paper, we compare Lasso with 5 other methods: Elastic net, SCAD, forward selection, thresholded Lasso, and forward backward selection. We measure their performances theoretically by the expected Hamming error, assuming that the regression coefficients are iid drawn from a two-point mixture and that the Gram matrix is block-wise diagonal. By deriving the rates of convergence of Hamming errors and the phase diagrams, we obtain useful conclusions about the pros and cons of different methods. |
Year | Venue | Keywords |
|---|---|---|
2022 | International Conference on Learning Representations (ICLR) | Lasso,Hamming error,phase diagram,rare and weak signals,elastic net,SCAD,thresholded Lasso,forward selection,forward backward selection |
DocType | ISSN | Citations |
Conference | Tenth International Conference on Learning Representations (ICLR
2022) | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Tracy Ke | 1 | 2 | 1.37 |
| Longlin Wang | 2 | 0 | 0.34 |