The impossibility region for detecting sparse mixtures using the higher criticism

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 joint null using the higher criticism (HC) statistics of these p-values. We calibrate the rare/weak model using parameters controlling the asymptotic distribution of nonnull 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 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.

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Publication Details

Journal
The Annals of Applied Probability
Published
2024-09-27
DOI
https://doi.org/10.1214/24-aap2086
Citations
2
Primary Topic
Statistical Methods and Inference
Type
article
Field-Weighted Citation Impact
0.71

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article

The impossibility region for detecting sparse mixtures using the higher criticism

David L. Donoho, Alon Kipnis
2 citations
The Annals of Applied Probability
Statistical Methods and Inference
0.71
article

The impossibility region for detecting sparse mixtures using the higher criticism

David L. Donoho, Alon Kipnis
article en
2 citations

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 joint null using the higher criticism (HC) statistics of these p-values. We calibrate the rare/weak model using parameters controlling the asymptotic distribution of nonnull 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 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.

The Annals of Applied ProbabilityVol. 34(5)
Brandman University (US), Stanford University (US)
National Science Foundation
Openalex Percentile: Top 34%
Statistical Methods and Inference
0.71
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The impossibility region for detecting sparse mixtures using the higher criticism — David L. Donoho, Alon Kipnis · The Annals of Applied Probability (2024) | TGRS Research Map | TGRS