Post-selection inference for L1-penalized likelihood models

Jonathan Taylor; Robert Tibshirani

Post-selection inference for L1-penalized likelihood models

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arxiv 1602.07358 v3 pith:2RPBLDUX submitted 2016-02-24 stat.ME

Jonathan Taylor , Robert Tibshirani This is my paper · ORCID

classification stat.ME

keywords lassomodelspost-selectioninferencelikelihoodmethodregressionapplications

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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.

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