Least Angle Regression

@misc{citeulike:1314374, abstract = {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;}, author = {Efron, Bradley and Hastie, Trevor and Johnstone, Iain and Tibshirani, Robert }, citeulike-article-id = {1314374}, eprint = {math.ST/0406456}, month = {Jun}, posted-at = {2007-05-21 06:01:52}, priority = {2}, title = {Least Angle Regression}, url = {http://arxiv.org/abs/math.ST/0406456}, year = {2004} }

TY - ELEC ID - citeulike:1314374 L3 - citeulike-article-id:1314374 TI - Least Angle Regression N2 - 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; AU - Efron, Bradley AU - Hastie, Trevor AU - Johnstone, Iain AU - Tibshirani, Robert PY - 2004/Jun/30/ UR - http://arxiv.org/abs/math.ST/0406456 ER -