Exact post-selection inference, with application to the lasso

Jason D. Lee, Dennis L. Sun, Yuekai Sun, Jonathan E. Taylor

Research output: Contribution to journalArticlepeer-review

398 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)907-927
Number of pages21
JournalAnnals of Statistics
Issue number3
StatePublished - Jun 2016
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


  • Confidence interval
  • Hypothesis test
  • Lasso
  • Model selection


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