The -test: leveraging sparsity in the Gaussian linear model for improved inference

We develop novel LASSO-based methods for coefficient testing and confidence interval construction in the Gaussian linear model with n≥ d. Our methods' finite-sample guarantees are identical to those of their ubiquitous ordinary-least-squares-t-test-based analogues, yet have substantially higher power when the true coefficient vector is sparse. In particular, our coefficient test, which we call the -test, performs like the one-sided t-test (despite not being given any information about the sign) under sparsity, and the corresponding confidence intervals are more than 10% shorter than the standard t-test based intervals. The nature of the -test directly provides a novel exact adjustment conditional on LASSO selection for post-selection inference, allowing for the construction of post-selection p-values and confidence intervals. None of our methods require resampling or Monte Carlo estimation. We perform a variety of simulations and a real data analysis on an HIV drug resistance data set to demonstrate the benefits of the -test. We end with a discussion of how the -test may asymptotically apply to a much more general class of parametric models.