7 articles
Graphical lasso
The graphical lasso (glasso) is an algorithm that estimates a sparse inverse covariance matrix using an l1 penalty, introduced by Friedman, Hastie, and Tibshirani in 2008.
Group lasso
The group lasso is a regularization method for linear and generalized linear regression that keeps or removes whole predefined groups of coefficients, introduced by Yuan and Lin in 2005.
Penalized regression
Penalized regression is a family of regression methods that adds a penalty on coefficient size to improve prediction, including ridge regression, the lasso, and the elastic net.
Regularized regression
Regularized regression adds a penalty term to least-squares fitting, shrinking coefficients toward zero to lower prediction error; ridge and the lasso are the canonical cases.
Sparse regression
Sparse regression fits a linear model while forcing most coefficients to be exactly zero, delivering both prediction and variable selection on high-dimensional data where ordinary least squares fails.
Stability selection
Stability selection is a statistical variable selection method, introduced by Meinshausen and Bühlmann in 2010, that runs the lasso on many random subsamples and keeps variables selected consistently.
Variable screening (statistics)
Variable screening is a fast statistical model-building step that ranks a large set of candidate predictors by marginal correlation and retains a smaller subset before formal variable selection.