The Annals of Statistics

Detection thresholds for the $\beta$-model on sparse graphs

Rajarshi Mukherjee, Sumit Mukherjee, and Subhabrata Sen

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In this paper, we study sharp thresholds for detecting sparse signals in $\beta$-models for potentially sparse random graphs. The results demonstrate interesting interplay between graph sparsity, signal sparsity and signal strength. In regimes of moderately dense signals, irrespective of graph sparsity, the detection thresholds mirror corresponding results in independent Gaussian sequence problems. For sparser signals, extreme graph sparsity implies that all tests are asymptotically powerless, irrespective of the signal strength. On the other hand, sharp detection thresholds are obtained, up to matching constants, on denser graphs. The phase transitions mentioned above are sharp. As a crucial ingredient, we study a version of the higher criticism test which is provably sharp up to optimal constants in the regime of sparse signals. The theoretical results are further verified by numerical simulations.

Article information

Ann. Statist., Volume 46, Number 3 (2018), 1288-1317.

Received: August 2016
Revised: May 2017
First available in Project Euclid: 3 May 2018

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Digital Object Identifier

Primary: 62G10: Hypothesis testing 62G20: Asymptotic properties 62C20: Minimax procedures

Detection boundary sparse random graphs beta model higher criticism sparse signals


Mukherjee, Rajarshi; Mukherjee, Sumit; Sen, Subhabrata. Detection thresholds for the $\beta$-model on sparse graphs. Ann. Statist. 46 (2018), no. 3, 1288--1317. doi:10.1214/17-AOS1585.

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Supplemental materials

  • Supplement to “Detection thresholds for the $\beta$ model on sparse graphs”. The supplementary material contain the proofs of additional technical results.