ABCD detects sparse, locally clustered distributional changes in image or high-dimensional time series by averaging graph-based scan statistics over multiple contiguous block sizes.
Spatial Multiresolution Cluster Detection Method
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abstract
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.
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stat.ME 1years
2025 1verdicts
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Adaptive Block-Based Change-Point Detection for Sparse Spatially Clustered Data with Applications in Remote Sensing Imaging
ABCD detects sparse, locally clustered distributional changes in image or high-dimensional time series by averaging graph-based scan statistics over multiple contiguous block sizes.