Coarse-to-fine spatial GLMM for scalable prediction and multiscale analysis
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866915974004867072 |
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| author | Murakami, Daisuke Comber, Alexis Yoshida, Takahiro Tsutsumida, Narumasa Brunsdon, Chris Nakaya, Tomoki |
| author_facet | Murakami, Daisuke Comber, Alexis Yoshida, Takahiro Tsutsumida, Narumasa Brunsdon, Chris Nakaya, Tomoki |
| contents | Although a recent study suggested that coarse-to-fine learning provides a fast and flexible framework for large-scale spatial process modeling, the method was originally developed for Gaussian responses, limiting its applicability. To address this limitation, we extended the coarse-to-fine spatial modeling (CFSM) framework to accommodate spatial generalized linear mixed models (GLMMs), with a particular focus on count data. The resulting model, referred to as CF-GLMM efficiently addresses the degeneracy problem often encountered in conventional spatial GLMMs. The performance of the proposed CF-GLMMs was evaluated in terms of spatial prediction and multiscale feature extraction via Monte Carlo experiments. Finally, we applied the proposed method to the analysis of coronavirus disease 2019 (COVID-19). The proposed method is implemented in an R package spCF (https://cran.r-project.org/web/packages/spCF/). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_01157 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Coarse-to-fine spatial GLMM for scalable prediction and multiscale analysis Murakami, Daisuke Comber, Alexis Yoshida, Takahiro Tsutsumida, Narumasa Brunsdon, Chris Nakaya, Tomoki Methodology Although a recent study suggested that coarse-to-fine learning provides a fast and flexible framework for large-scale spatial process modeling, the method was originally developed for Gaussian responses, limiting its applicability. To address this limitation, we extended the coarse-to-fine spatial modeling (CFSM) framework to accommodate spatial generalized linear mixed models (GLMMs), with a particular focus on count data. The resulting model, referred to as CF-GLMM efficiently addresses the degeneracy problem often encountered in conventional spatial GLMMs. The performance of the proposed CF-GLMMs was evaluated in terms of spatial prediction and multiscale feature extraction via Monte Carlo experiments. Finally, we applied the proposed method to the analysis of coronavirus disease 2019 (COVID-19). The proposed method is implemented in an R package spCF (https://cran.r-project.org/web/packages/spCF/). |
| title | Coarse-to-fine spatial GLMM for scalable prediction and multiscale analysis |
| topic | Methodology |
| url | https://arxiv.org/abs/2605.01157 |