Post-Selection Inference in Generalized Linear Models via Parametric Programming

We propose a unified framework to draw inferences for regression coefficients in a generalized linear model (GLM) following Lasso-based variable selection.We adapt to non-Gaussian GLMs a recently developed parametric programming strategy for postselection inference in the linear model with a Gaussian response by drawing parallels between maximum likelihood estimation in GLMs and least squares estimation in linear models.We then conduct post-selection inference based on a linearized model for pse