Exact post-selection inference, with application to the lasso
This addresses the challenge of reliable inference post-model selection for statisticians and data scientists, representing a foundational advancement rather than an incremental improvement.
The paper tackles the problem of valid statistical inference after model selection, specifically applying it to the lasso to form confidence intervals for selected coefficients and test for inclusion of all relevant variables.
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.