1.9k citations · 3.6k across the 13 of their papers we have counts for
4 papers · 1 filter
Principal component-guided sparse regression
J. Kenneth Tay, Jerome Friedman, Robert Tibshirani
We propose a new method for supervised learning, especially suited to wide data where the number of features is much greater than the number of observations. The method combines th…
A Pliable Lasso
Robert Tibshirani, Jerome Friedman
We propose a generalization of the lasso that allows the model coefficients to vary as a function of a general set of modifying variables. These modifiers might be variables such a…
A Study of Error Variance Estimation in Lasso Regression
Stephen Reid, Robert Tibshirani, Jerome Friedman
Variance estimation in the linear model when is a difficult problem. Standard least squares estimation techniques do not apply. Several variance estimators have been propos…
A new graph-based two-sample test for multivariate and object data
Hao Chen, Jerome H. Friedman
Two-sample tests for multivariate data and especially for non-Euclidean data are not well explored. This paper presents a novel test statistic based on a similarity graph construct…