Spatially varying coefficient model for neuroimaging data with jump discontinuities

Hongtu Zhu Jianqing Fan Linglong Kong

Statistics Theory and Methods mathscidoc:1912.43325

Journal of the American Statistical Association, 109, (507), 1084-1098
Motivated by recent work on studying massive imaging data in various neuroimaging studies, we propose a novel spatially varying coefficient model (SVCM) to capture the varying association between imaging measures in a three-dimensional volume (or two-dimensional surface) with a set of covariates. Two stylized features of neuorimaging data are the presence of multiple piecewise smooth regions with unknown edges and jumps and substantial spatial correlations. To specifically account for these two features, SVCM includes a measurement model with multiple varying coefficient functions, a jumping surface model for each varying coefficient function, and a functional principal component model. We develop a three-stage estimation procedure to simultaneously estimate the varying coefficient functions and the spatial correlations. The estimation procedure includes a fast multiscale adaptive estimation and
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@inproceedings{hongtuspatially,
  title={Spatially varying coefficient model for neuroimaging data with jump discontinuities},
  author={Hongtu Zhu, Jianqing Fan, and Linglong Kong},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113723159184885},
  booktitle={Journal of the American Statistical Association},
  volume={109},
  number={507},
  pages={1084-1098},
}
Hongtu Zhu, Jianqing Fan, and Linglong Kong. Spatially varying coefficient model for neuroimaging data with jump discontinuities. Vol. 109. In Journal of the American Statistical Association. pp.1084-1098. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113723159184885.
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