1.9k citations · 3.2k across the 7 of their papers we have counts for
7 papers
Testing significance of features by lassoed principal components
Daniela M. Witten, Robert Tibshirani
We consider the problem of testing the significance of features in high-dimensional settings. In particular, we test for differentially-expressed genes in a microarray experiment.…
A study of pre-validation
Holger Höfling, Robert Tibshirani
Given a predictor of outcome derived from a high-dimensional dataset, pre-validation is a useful technique for comparing it to competing predictors on the same dataset. For microar…
Discussion of: Treelets--An adaptive multi-scale basis for sparse unordered data
Robert Tibshirani
Discussion of "Treelets--An adaptive multi-scale basis for sparse unordered data" [arXiv:0707.0481]
On the "degrees of freedom" of the lasso
Hui Zou, Trevor Hastie, Robert Tibshirani
We study the effective degrees of freedom of the lasso in the framework of Stein's unbiased risk estimation (SURE). We show that the number of nonzero coefficients is an unbiased e…
Sparse inverse covariance estimation with the lasso
Jerome Friedman, Trevor Hastie, Robert Tibshirani
We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a si…
Pathwise coordinate optimization
Jerome Friedman, Trevor Hastie, Holger Höfling +1
We consider ``one-at-a-time'' coordinate-wise descent algorithms for a class of convex optimization problems. An algorithm of this kind has been proposed for the -penalized re…