Algorithms for simultaneous shrinkage and selection in regression and classification provide attractive solutions to knotty old statistical challenges. Nevertheless, as far as we can tell, Tibshirani's Lasso algorithm has had little impact on statistical practice. Two particular reasons for this may be the relative inefficiency of the original Lasso algorithm and the relative complexity of more recent Lasso algorithms [e.g., Osborne, Presnell and Turlach (2000)]. Efron, Hastie, Johnstone and Tibshirani have provided an efficient, simple algorithm for the Lasso as well as algorithms for stagewise regression and the new least angle regression. As such this paper is an important contribution to statistical computing
We consider the least angle regression and forward stagewise algorithms for solving penalized least...
The Lasso achieves variance reduction and variable selection by solving an ℓ 1 -regularized least sq...
An important problem in data science and statistical learning is to predict an outcome based on data...
The purpose of model selection algorithms such as All Subsets, Forward Selection, and Backward Elimi...
The issue of model selection has drawn the attention of both applied and theoretical statisticians f...
Multicollinearity often occurs in regression analysis. Multicollinearity is a condition of correlati...
The least absolute deviation (LAD) regression is a useful method for robust regression, and the leas...
In this paper, we propose a new method which is a modified group lasso with least angle regression s...
We investigate multiple testing and variable selection using the Least Angle Regression (LARS) algor...
We consider the linear regression problem. We propose the S-Lasso procedure to estimate the unknown ...
We propose inference tools for least angle regression and the lasso, from the joint distribution of ...
As lasso regression has grown exceedingly popular as a tool for coping with variable selection in hi...
We consider the least angle regression and forward stagewise algorithms for solving penalized least ...
The least absolute shrinkage and selection operator ('lasso') has been widely used in regr...
Both classical Forward Selection and the more modern Lasso provide compu-tationally feasible methods...
We consider the least angle regression and forward stagewise algorithms for solving penalized least...
The Lasso achieves variance reduction and variable selection by solving an ℓ 1 -regularized least sq...
An important problem in data science and statistical learning is to predict an outcome based on data...
The purpose of model selection algorithms such as All Subsets, Forward Selection, and Backward Elimi...
The issue of model selection has drawn the attention of both applied and theoretical statisticians f...
Multicollinearity often occurs in regression analysis. Multicollinearity is a condition of correlati...
The least absolute deviation (LAD) regression is a useful method for robust regression, and the leas...
In this paper, we propose a new method which is a modified group lasso with least angle regression s...
We investigate multiple testing and variable selection using the Least Angle Regression (LARS) algor...
We consider the linear regression problem. We propose the S-Lasso procedure to estimate the unknown ...
We propose inference tools for least angle regression and the lasso, from the joint distribution of ...
As lasso regression has grown exceedingly popular as a tool for coping with variable selection in hi...
We consider the least angle regression and forward stagewise algorithms for solving penalized least ...
The least absolute shrinkage and selection operator ('lasso') has been widely used in regr...
Both classical Forward Selection and the more modern Lasso provide compu-tationally feasible methods...
We consider the least angle regression and forward stagewise algorithms for solving penalized least...
The Lasso achieves variance reduction and variable selection by solving an ℓ 1 -regularized least sq...
An important problem in data science and statistical learning is to predict an outcome based on data...