Spatial prediction of counts and rates
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Updated: Feb 16, 2026
Spatial Molecular Imaging of the Glycome Using Mass Spectrometry
Published on: November 28, 2025
Carol A Gotway1, Russell D Wolfinger
1National Center for Environmental Health, Mailstop E70, Centers for Disease Control and Prevention, 1600 Clifton Road, NE, Atlanta, GA 30333, USA. cdg7@cdc.gov
Abstract:
In this paper we provide both theoretical and empirical comparisons of marginal and conditional methods for analysing spatial count data. We focus on methods for spatial prediction developed from a generalized linear mixed model framework and compare them with the traditional linear (kriging) predictor. Prediction methods are illustrated and compared through a case study based on real data and through a detailed simulation study. The paper emphasizes a better understanding of the strengths and weaknesses of each approach.
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