Statistical Science

Higher Criticism for Large-Scale Inference, Especially for Rare and Weak Effects

David Donoho and Jiashun Jin

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Abstract

In modern high-throughput data analysis, researchers perform a large number of statistical tests, expecting to find perhaps a small fraction of significant effects against a predominantly null background. Higher Criticism (HC) was introduced to determine whether there are any nonzero effects; more recently, it was applied to feature selection, where it provides a method for selecting useful predictive features from a large body of potentially useful features, among which only a rare few will prove truly useful.

In this article, we review the basics of HC in both the testing and feature selection settings. HC is a flexible idea, which adapts easily to new situations; we point out simple adaptions to clique detection and bivariate outlier detection. HC, although still early in its development, is seeing increasing interest from practitioners; we illustrate this with worked examples. HC is computationally effective, which gives it a nice leverage in the increasingly more relevant “Big Data” settings we see today.

We also review the underlying theoretical “ideology” behind HC. The Rare/Weak (RW) model is a theoretical framework simultaneously controlling the size and prevalence of useful/significant items among the useless/null bulk. The RW model shows that HC has important advantages over better known procedures such as False Discovery Rate (FDR) control and Family-wise Error control (FwER), in particular, certain optimality properties. We discuss the rare/weak phase diagram, a way to visualize clearly the class of RW settings where the true signals are so rare or so weak that detection and feature selection are simply impossible, and a way to understand the known optimality properties of HC.

Article information

Source
Statist. Sci., Volume 30, Number 1 (2015), 1-25.

Dates
First available in Project Euclid: 4 March 2015

Permanent link to this document
https://projecteuclid.org/euclid.ss/1425492437

Digital Object Identifier
doi:10.1214/14-STS506

Mathematical Reviews number (MathSciNet)
MR3317751

Zentralblatt MATH identifier
1332.62019

Keywords
Classification control of FDR feature selection Higher Criticism large covariance matrix large-scale inference rare and weak effects phase diagram sparse signal detection

Citation

Donoho, David; Jin, Jiashun. Higher Criticism for Large-Scale Inference, Especially for Rare and Weak Effects. Statist. Sci. 30 (2015), no. 1, 1--25. doi:10.1214/14-STS506. https://projecteuclid.org/euclid.ss/1425492437


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