Scalable Predictions for Spatial Probit Linear Mixed Models Using Nearest Neighbor Gaussian Processes

Spatial probit generalized linear mixed models (spGLMM) with a linear fixed effect and a spatial random effect, endowed with a Gaussian Process prior, are widely used for analysis of binary spatial data. However, the canonical Bayesian implementation of this hierarchical mixed model can involve prot...

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Detalles Bibliográficos
Autores principales: Saha, Arkajyoti, Datta, Abhirup, Banerjee, Sudipto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10544813/
https://www.ncbi.nlm.nih.gov/pubmed/37786782
http://dx.doi.org/10.6339/22-jds1073

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10544813/
https://www.ncbi.nlm.nih.gov/pubmed/37786782
http://dx.doi.org/10.6339/22-jds1073

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