The Annals of Statistics

Multiple testing of local maxima for detection of peaks in random fields

Dan Cheng and Armin Schwartzman

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Abstract

A topological multiple testing scheme is presented for detecting peaks in images under stationary ergodic Gaussian noise, where tests are performed at local maxima of the smoothed observed signals. The procedure generalizes the one-dimensional scheme of Schwartzman, Gavrilov and Adler [Ann. Statist. 39 (2011) 3290–3319] to Euclidean domains of arbitrary dimension. Two methods are developed according to two different ways of computing p-values: (i) using the exact distribution of the height of local maxima, available explicitly when the noise field is isotropic [Extremes 18 (2015) 213–240; Expected number and height distribution of critical points of smooth isotropic Gaussian random fields (2015) Preprint]; (ii) using an approximation to the overshoot distribution of local maxima above a pre-threshold, applicable when the exact distribution is unknown, such as when the stationary noise field is nonisotropic [Extremes 18 (2015) 213–240]. The algorithms, combined with the Benjamini–Hochberg procedure for thresholding p-values, provide asymptotic strong control of the False Discovery Rate (FDR) and power consistency, with specific rates, as the search space and signal strength get large. The optimal smoothing bandwidth and optimal pre-threshold are obtained to achieve maximum power. Simulations show that FDR levels are maintained in nonasymptotic conditions. The methods are illustrated in the analysis of functional magnetic resonance images of the brain.

Article information

Source
Ann. Statist., Volume 45, Number 2 (2017), 529-556.

Dates
Received: May 2014
Revised: February 2016
First available in Project Euclid: 16 May 2017

Permanent link to this document
https://projecteuclid.org/euclid.aos/1494921949

Digital Object Identifier
doi:10.1214/16-AOS1458

Mathematical Reviews number (MathSciNet)
MR3650392

Zentralblatt MATH identifier
1369.62144

Subjects
Primary: 62H35: Image analysis
Secondary: 62H15: Hypothesis testing

Keywords
False discovery rate Gaussian random field kernel smoothing image analysis overshoot distribution selective inference topological inference

Citation

Cheng, Dan; Schwartzman, Armin. Multiple testing of local maxima for detection of peaks in random fields. Ann. Statist. 45 (2017), no. 2, 529--556. doi:10.1214/16-AOS1458. https://projecteuclid.org/euclid.aos/1494921949


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Supplemental materials

  • Online Supplement to “Multiple testing of local maxima for detection of peaks in random fields”. In this supplement, we provide proofs for Lemma 4 and Theorems 3, 5, 8, 10 and 11.