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Updated: Oct 14, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Discussion on Competition for Spatial Statistics for Large Datasets
Roman Flury1, Reinhard Furrer1,2
1Department of Mathematics, University of Zurich, Zurich, Switzerland.
Abstract:
We discuss the experiences and results of the AppStatUZH team's participation in the comprehensive and unbiased comparison of different spatial approximations conducted in the Competition for Spatial Statistics for Large Datasets. In each of the different sub-competitions, we estimated parameters of the covariance model based on a likelihood function and predicted missing observations with simple kriging. We approximated the covariance model either with covariance tapering or a compactly supported Wendland covariance function.
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