Post-Selection Inference for ℓ1-Penalized Likelihood Models
By Jonathan E. Taylor, Robert Tibshirani. 2017
183 citations recorded by the source corpus.
We present a new method for post‐selection inference for ℓ1 (lasso)'penalized likelihood models, including generalized regression models. Our approach generalizes the post‐selection framework presented in Lee et al. (2013). The method provides P‐values and confidence intervals that are asymptotically valid, conditional on the inherent selection done by the lasso. We present applications of this work to (regularized) logistic regression, Cox's proportional hazards model, and the graphical lasso. We do not provide rigorous proofs here of the claimed results, but rather conceptual and theoretical sketches. The Canadian Journal of Statistics 46: 41–61; 2018 © 2017 Statistical Society of Canada
Subfields: Statistics and Probability
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Citations in the corpus
- Forward stability and model path selection (2021, neutral): “Finally, we are not seeking to select a particular model and adjust variance estimates to account for additional uncertainty as might be done via some form of post-selection inference (e.g. Berk et al. 2013; Lee et al. 2016; Tibshi-rani et al. 2016; Taylor and Tibshirani 2018).”
- Uniformly valid confidence intervals post-model-selection (2016, methodology): “This confidence interval is developed for the lasso model selector and has some asymptotic guarantees which are discussed in Taylor and Tibshirani (2017).”