High-dimensional robust regression under heavy-tailed data: Asymptotics and Universality
This work addresses robust statistical estimation for high-dimensional data analysis, providing theoretical insights but is incremental in extending existing asymptotic frameworks.
The paper tackles robust regression in high dimensions with heavy-tailed data, showing that Huber loss with optimal tuning is suboptimal and ridge regression can have faster decay rates when covariates lack finite second moments.
We investigate the high-dimensional properties of robust regression estimators in the presence of heavy-tailed contamination of both the covariates and response functions. In particular, we provide a sharp asymptotic characterisation of M-estimators trained on a family of elliptical covariate and noise data distributions including cases where second and higher moments do not exist. We show that, despite being consistent, the Huber loss with optimally tuned location parameter $δ$ is suboptimal in the high-dimensional regime in the presence of heavy-tailed noise, highlighting the necessity of further regularisation to achieve optimal performance. This result also uncovers the existence of a transition in $δ$ as a function of the sample complexity and contamination. Moreover, we derive the decay rates for the excess risk of ridge regression. We show that, while it is both optimal and universal for covariate distributions with finite second moment, its decay rate can be considerably faster when the covariates' second moment does not exist. Finally, we show that our formulas readily generalise to a richer family of models and data distributions, such as generalised linear estimation with arbitrary convex regularisation trained on mixture models.