Robust multivariate least angle regression

Robust groupwise least angle regression

Many regression problems exhibit a natural grouping among predictor variables. Examples are groups of dummy variables representing categorical variables, or present and lagged values of time series data. Since model selection in such cases typically aims for selecting groups of variables rather than individual covariates, an extension of the popular least angle regression (LARS) procedure to gr...

Least Angle Regression

The purpose of model selection algorithms such as All Subsets, Forward Selection, and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope to select a parsimonious set for the efficient prediction of a response variable. Least Angle Regres...

Discussion of “ Least Angle Regression ”

Being able to reliably, and automatically, select variables in linear regression models is a notoriously difficult problem. This research attacks this question head on, introducing not only a computationally efficient algorithm and method, LARS (and its derivatives), but at the same time introducing comprehensive theory explaining the intricate details of the procedure as well as theory to guid...

Discussion of Least Angle Regression

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 mor...

Robust Multivariate Regression