Bayesian spatial transformation models with applications in neuroimaging data.

@article{Miranda2013BayesianST,
  title={Bayesian spatial transformation models with applications in neuroimaging data.},
  author={Michelle Ferreira Miranda and Hongtu Zhu and Joseph G. Ibrahim},
  journal={Biometrics},
  year={2013},
  volume={69 4},
  pages={1074-83}
}
The aim of this article 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. The proposed STM include a varying Box-Cox transformation model for dealing with the issue of non-Gaussian distributed imaging data and a Gaussian Markov random field model for incorporating spatial smoothness of the imaging data. Posterior computation proceeds via an… CONTINUE READING
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