Geostatistical modeling of positive definite matrices: An application to diffusion tensor imaging.

@article{Lan2021GeostatisticalMO,
  title={Geostatistical modeling of positive definite matrices: An application to diffusion tensor imaging.},
  author={Zhou Lan and Brian J. Reich and Joseph Guinness and Dipankar Bandyopadhyay and Liangsuo Ma and F. Gerard Moeller},
  journal={Biometrics},
  year={2021}
}
Geostatistical modeling for continuous point-referenced data has been extensively applied to neuroimaging because it produces efficient and valid statistical inference. However, diffusion tensor imaging (DTI), a neuroimaging technique characterizing the brain's anatomical structure, produces a positive definite (p.d.) matrix for each voxel. Currently, only a few geostatistical models for p.d. matrices have been proposed because introducing spatial dependence among p.d. matrices properly is… Expand

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