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- W2897038751 abstract "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 (2014). 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." @default.
- W2897038751 created "2018-10-26" @default.
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- W2897038751 date "2016-02-24" @default.
- W2897038751 modified "2023-09-27" @default.
- W2897038751 title "Post-selection inference L1-penalized likelihood models" @default.
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