Robust Signal Detection with Quadratically Convex Orthosymmetric Constraints
Authors
Yikun Li
Affiliation: Department of Statistics and Data Science, Northwestern University
Email: YikunLi2028@u.northwestern.edu
Matey Neykov
Affiliation: Department of Statistics and Data Science, Northwestern University
Email: mneykov@northwestern.edu
Abstract
This paper studies the problem of robust signal detection in Gaussian noise under quadratically convex orthosymmetric (QCO) constraints. We consider a minimax testing framework where the signal belongs to a QCO set and is separated from zero in Euclidean norm, while an adversary is allowed to arbitrarily corrupt a fraction $\epsilon $ of the samples. We establish the minimax separation radius between the null and alternative purely in terms of the constraint geometry, sample size, corruption rate, and noise scale. Our analysis argues that the Kolmogorov widths of the constraint set play a central role in determining the detection limits, paralleling to classic results in estimation problem. The derived lower bounds exhibit phase transitions with respect to the corruption rate and confirm that robust testing is statistically easier than robust estimation. While the statistically optimal rate is achieved by a computationally intractable test, we develop a polynomial-time algorithm that matches the information-theoretic lower bound up to logarithmic factors. Unlike prior work, our algorithm handles signals of arbitrary Euclidean length while respecting the QCO constraints. Finally, we extend these results to the robust $\ell _{p}$ norm testing for $1 \le p < 2$.
Availability
The preprint of this work is available at https://arxiv.org/abs/2308.13036, or you can find the up-to-date (even contains some fixes that are not included in the arXiv version) PDF file here.