CF-GLMM extends the coarse-to-fine spatial modeling framework to GLMMs for count data, delivering scalable prediction, multiscale feature extraction, and resolution of degeneracy problems in conventional spatial GLMMs.
For example, even if the true spatial process consists of both small- and large- scale components with distinct interpretations, they cannot be identified separately
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Coarse-to-fine spatial GLMM for scalable prediction and multiscale analysis
CF-GLMM extends the coarse-to-fine spatial modeling framework to GLMMs for count data, delivering scalable prediction, multiscale feature extraction, and resolution of degeneracy problems in conventional spatial GLMMs.