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

ABSTRACT: Geostatistical modeling for continuous point-referenced data has extensively been 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 challenging. In this paper, we use the spatial Wishart process, a spatial stochastic process (random field), where each p.d. matrix-variate random variable marginally follows a Wishart distribution, and spatial dependence between random matrices is induced by latent Gaussian processes. This process is va

SUBMITTER: Lan Z 

PROVIDER: S-EPMC10294277 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature