English

The Impossibility Region for Detecting Sparse Mixtures using the Higher Criticism

Statistics Theory 2021-10-20 v2 Statistics Theory

Abstract

Consider a multiple hypothesis testing setting involving rare/weak effects: relatively few tests, out of possibly many, deviate from their null hypothesis behavior. Summarizing the significance of each test by a P-value, we construct a global test against the null using the Higher Criticism (HC) statistics of these P-values. We calibrate the rare/weak model using parameters controlling the asymptotic distribution of non-null P-values near zero. We derive a region in the parameter space where the HC test is asymptotically powerless. Our derivation involves very different tools than previously used to show the powerlessness of HC, relying on properties of the empirical processes underlying HC. In particular, our result applies to situations where HC is not asymptotically optimal, or when the asymptotically detectable region of the parameter space is unknown.

Keywords

Cite

@article{arxiv.2103.03218,
  title  = {The Impossibility Region for Detecting Sparse Mixtures using the Higher Criticism},
  author = {David L. Donoho and Alon Kipnis},
  journal= {arXiv preprint arXiv:2103.03218},
  year   = {2021}
}
R2 v1 2026-06-23T23:46:00.749Z