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.
∈{𝑠!,…,𝑠5} randomly distributed within the region [0, 1] × [0, 1]. A sample was generated for each site as follows: 𝑦(𝑠
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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.