Exact post-selection inference, with application to the lasso
Statistics Theory
2016-05-04 v8 Methodology
Machine Learning
Statistics Theory
Abstract
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 conditioned on the selection event. We specialize the approach to model selection by the lasso to form valid confidence intervals for the selected coefficients and test whether all relevant variables have been included in the model.
Cite
@article{arxiv.1311.6238,
title = {Exact post-selection inference, with application to the lasso},
author = {Jason D. Lee and Dennis L. Sun and Yuekai Sun and Jonathan E. Taylor},
journal= {arXiv preprint arXiv:1311.6238},
year = {2016}
}
Comments
Published at http://dx.doi.org/10.1214/15-AOS1371 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)