Spatial Multiresolution Cluster Detection Method
This addresses the challenge of detecting irregular clusters in large spatial datasets, such as in neuroimaging, with an incremental improvement over prior methods.
The paper tackles the problem of identifying irregularly shaped clusters in spatial data by proposing a novel multi-resolution cluster detection (MCD) method, which is shown to be more effective than existing methods in simulations and f-MRI data without requiring heavy computation.
A novel multi-resolution cluster detection (MCD) method is proposed to identify irregularly shaped clusters in space. Multi-scale test statistic on a single cell is derived based on likelihood ratio statistic for Bernoulli sequence, Poisson sequence and Normal sequence. A neighborhood variability measure is defined to select the optimal test threshold. The MCD method is compared with single scale testing methods controlling for false discovery rate and the spatial scan statistics using simulation and f-MRI data. The MCD method is shown to be more effective for discovering irregularly shaped clusters, and the implementation of this method does not require heavy computation, making it suitable for cluster detection for large spatial data.