# The L-test: Increasing the Linear Model F-test's Power Under Sparsity Without Sacrificing Validity

arXiv: https://arxiv.org/abs/2511.23466 · HTML: https://mlsanity.com/paper/2511.23466/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2025.

## Built on by (most cited first, 1 of 1)

- [Selective Inference for Clustering Under Unknown Covariance](https://mlsanity.com/paper/2610.17458/) (2026)

## Abstract (arXiv)

We introduce a new procedure for testing the significance of a set of regression coefficients in a Gaussian linear model with n ≥ d. Our method, the L-test, provides the same statistical validity guarantee as the classical F-test, while attaining higher power when the nuisance coefficients are sparse. Although the L-test requires Monte Carlo sampling, each sample's runtime is dominated by simple matrix-vector multiplications so that the overall test remains computationally efficient. Furthermore, we provide a Monte-Carlo-free variant that can be used for particularly large-scale multiple testing applications. We give intuition for the power of our approach, validate its advantages through extensive simulations, and illustrate its practical utility in both single- and multiple-testing contexts with an application to an HIV drug resistance dataset. In the concluding remarks, we also discuss how our methodology can be applied to a more general class of parametric models that admit asymptotically Gaussian estimators.