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Bayesian spatial transformation models with applications in neuroimaging data
The aim of this paper is to develop a class of spatial transformation models (STM) to spatially model the varying association between imaging measures in a three-dimensional (3D) volume (or 2D surface) and a set of covariates. Our STMs include a varying Box-Cox transformation model for dealing with...
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| Autori principali: | , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2013
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3864982/ https://ncbi.nlm.nih.gov/pubmed/24128143 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1111/biom.12085 |
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