We present a new method for post‐selection inference for (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

中文翻译：

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ℓ1 惩罚似然模型的选择后推断。


我们提出了一种新的选择后推理方法 （套索）'惩罚似然模型，包括广义回归模型。我们的方法概括了 Lee 等人提出的选择后框架。 （2013）。该方法提供渐近有效的P值和置信区间，以套索进行的固有选择为条件。我们将这项工作应用于（正则化）逻辑回归、Cox 比例风险模型和图形套索。我们在这里不提供所声称结果的严格证明，而是提供概念和理论草图。加拿大统计杂志46：41-61； 2018 © 2017 加拿大统计学会