Year: 2025
Type: preprint
Abstract: In this work we develop a Monte Carlo method to compute the height distribution of local maxima of a stationary Gaussian or Gaussian-related random field that is observed on a regular lattice. We show that our method can be used to provide valid peak based inference in datasets with low levels of smoothness, where existing formulae derived for continuous domains are not accurate. We also extend the methods in Worsley (2005) and Taylor et al. (2007) to compute the peak height distribution and compare them with our approach. Lastly, we apply our method to a task fMRI dataset to show how it can be used in practice.
Source: arXiv (Cornell University)
Language: English
Subfield: Applied Mathematics
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funder: National Institutes of Health
Awards: G9000089182, G9000173539
OpenAlex ID: W4406785315