565 citations · 3.2k across the 41 of their papers we have counts for
5 papers · 1 filter
Exact post-selection inference, with application to the lasso
Jason D. Lee, Dennis L. Sun, Yuekai Sun +1
We develop a general approach to valid inference after model selection. At the core of our framework is a result that characterizes the distribution of a post-selection estimator c…
Tests in adaptive regression via the Kac-Rice formula
Jonathan Taylor, Joshua Loftus, Ryan Tibshirani
We derive an exact p-value for testing a global null hypothesis in a general adaptive regression problem. The general approach uses the Kac-Rice formula, as described in (Adler & T…
Adaptive testing for the graphical lasso
Max Grazier G'Sell, Jonathan Taylor, Robert Tibshirani
We consider tests of significance in the setting of the graphical lasso for inverse covariance matrix estimation. We propose a simple test statistic based on a subsequence of the k…
On model selection consistency of regularized M-estimators
Jason D. Lee, Yuekai Sun, Jonathan E. Taylor
Regularized M-estimators are used in diverse areas of science and engineering to fit high-dimensional models with some low-dimensional structure. Usually the low-dimensional struct…
A significance test for the lasso
Richard Lockhart, Jonathan Taylor, Ryan J. Tibshirani +1
In the sparse linear regression setting, we consider testing the significance of the predictor variable that enters the current lasso model, in the sequence of models visited along…