摘要：We study tools for inference conditioned on model selection events that are defined by the generalized lasso regularization path. The generalized lasso estimate is given by the solution of a penalized least squares regression problem, where the penalty is the $\ell_{1}$ norm of a matrix $D$ times the coefficient vector. The generalized lasso path collects these estimates as the penalty parameter $\lambda$ varies (from $\infty$ down to 0). Leveraging a (sequential) characterization of this path from Tibshirani and Taylor [37], and recent advances in post-selection inference from Lee at al. [22], Tibshirani et al. [38], we develop exact hypothesis tests and confidence intervals for linear contrasts of the underlying mean vector, conditioned on any model selection event along the generalized lasso path (assuming Gaussian errors in the observations).
