Least Angle Regression
Loading...
Date
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Description
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 Regression (LARS), a new model selection algorithm, is a useful and less greedy version of traditional forward selection methods.
Three main properties are derived: (1) A simple modification of the LARS algorithm implements the Lasso, an attractive version of ordinary least squares that constrains the sum of the absolute regression coefficients; the LARS modification calculates all possible Lasso estimates for a given problem, using an order of magnitude less computer time than previous methods.
(2) A different LARS modification efficiently implements Forward Stagewise linear regression, another promising new model selection method;
This paper discussed in: math.ST/0406463, math.ST/0406467, math.ST/0406468, math.ST/0406469, math.ST/0406470, math.ST/0406471, math.ST/0406472, math.ST/0406473. Rejoinder in math.ST/0406474
This paper discussed in: math.ST/0406463, math.ST/0406467, math.ST/0406468, math.ST/0406469, math.ST/0406470, math.ST/0406471, math.ST/0406472, math.ST/0406473. Rejoinder in math.ST/0406474