Least Angle Regression Export

@misc{citeulike:1293074, 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 eificient pre- diction of a response variable. Least Angle Regression (\" LARS\"), a new model selection algorithm, is a useful and less greedy version of...}, author = {Efron, B. and Hastie, T. and Johnstone, I. and Tibshirani, R.}, citeulike-article-id = {1293074}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.57.8168}, keywords = {feature, lasso, regression, selection, variable}, posted-at = {2008-10-09 11:58:24}, priority = {4}, title = {Least Angle Regression}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.57.8168}, year = {2002} }

TY - GEN ID - citeulike:1293074 L3 - citeulike-article-id:1293074 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 eificient pre- diction of a response variable. Least Angle Regression (" LARS"), a new model selection algorithm, is a useful and less greedy version of... TI - Least Angle Regression KW - feature KW - lasso KW - regression KW - selection KW - variable AU - Efron, B AU - Hastie, T AU - Johnstone, I AU - Tibshirani, R PY - 2002/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.57.8168 ER -