Comparing heart PET scans: an adjustment of Kolmogorov-Smirnov test under spatial autocorrelation.

The principle of independence is a fundamental yet often disregarded assumption in statistical inference. It is observed that the implications of correlations, if not considered, can lead to a conservative estimation of Type I error in the presence of positive linear correlations when utilizing the Kolmogorov-Smirnov (KS) test. Conversely, negative linear correlations may engender a liberal estimation of Type I error. To address the impact of spatial autocorrelation in the analysis of Positron Emission Tomography (PET) images, we have proposed an innovative methodology to reconstruct a grid map of human heart scans using spherical coordinates. We have examined the distribution of the KS test statistic under spatial autocorrelation through Monte Carlo (MC) simulation and have introduced a KS test with a spatial adjustment. The newly proposed KS test with spatial adjustment demonstrates a controlled Type I error and power that is not inferior when compared to the original KS test. This suggests its potential utility in the analysis of spatially autocorrelated data. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

Citation

Wenjun Zheng, Hongjian Zhu, K Lance Gould, Dejian Lai. Comparing heart PET scans: an adjustment of Kolmogorov-Smirnov test under spatial autocorrelation. Journal of applied statistics. 2025;52(1):253-269

PMID: 39811082

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