# Robust Distribution‐Free Tests for the Linear Model

## TL;DR

This paper introduces a new statistical method for testing associations in biological data affected by skewed or heavy-tailed noise.

## Contribution

The paper introduces RobustPALMRT, a permutation framework that improves type I error control and expands testing capabilities in linear models.

## Key findings

- RobustPALMRT maintains type I error control even with heavy-tailed or skewed noise.
- Using robust loss functions in model evaluation improves performance regardless of model fitting method.
- The method reveals novel differences in Long-COVID patient data despite highly skewed noise.

## Abstract

Recently, there has been growing concern about heavy‐tailed and skewed noise in biological data. We introduce RobustPALMRT, a flexible permutation framework for testing the association of a covariate of interest adjusted for control covariates. RobustPALMRT controls type I error rate for finite‐samples, even in the presence of heavy‐tailed or skewed noise. The new framework expands the scope of state‐of‐the‐art tests in three directions. First, our method applies to robust and quantile regressions, even with the necessary hyper‐parameter tuning. Second, by separating model‐fitting and model‐evaluation, we discover that performance improves when using a robust loss function in the model‐evaluation step, regardless of how the model is fit. Third, we allow fitting multiple models to detect specialized features of interest in a distribution. To demonstrate this, we introduce DispersionPALMRT, which tests for differences in dispersion between treatment and control groups. We establish theoretical guarantees, identify settings where our method has greater power than existing methods, and analyze existing immunological data on Long‐COVID patients. Using RobustPALMRT, we unveil novel differences between Long‐COVID patients and others even in the presence of highly skewed noise.