  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.
