Exact post-selection inference, with application to the lasso
Jason D. [ Dennis L. [ Yuekai [ Jonathan E. [ University of California, Berkeley\thanksmarkM1, California Polytechnic State University\thanksmarkM2 and Stanford University\thanksmarkM3 J. D. Lee University of California, Berkeley 465 Soda Hall Berkeley, California 94720 USA D. L. Sun California Polytechnic State University Building 25, Room 107D San Luis Obispo, California USA Y. Sun University of California, Berkeley 367 Evans Hall Berkeley, California 94720 USA J. E. Taylor Stanford University 390 Serra Mall Stanford, California USA
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.
原文 arXiv:1311.6238;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1311.6238v8