Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping

BACKGROUND: Disease maps of crude rates from routinely collected health data indexed at a small geographical resolution pose specific statistical problems due to the sparse nature of the data. Spatial smoothers allow areas to borrow strength from neighboring regions to produce a more stable estimate...

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Detalles Bibliográficos
Autores principales: Hampton, Kristen H, Serre, Marc L, Gesink, Dionne C, Pilcher, Christopher D, Miller, William C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Methodology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3204220/
https://www.ncbi.nlm.nih.gov/pubmed/21978359
http://dx.doi.org/10.1186/1476-072X-10-54

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3204220/
https://www.ncbi.nlm.nih.gov/pubmed/21978359
http://dx.doi.org/10.1186/1476-072X-10-54

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