|Title||Tensor Regression with Applications in Neuroimaging Data Analysis.|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Zhou, Hua, Lexin Li, and Hongtu Zhu|
|Journal||J Am Stat Assoc|
Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates of more complex form such as multidimensional arrays (tensors). Traditional statistical and computational methods are proving insufficient for analysis of these high-throughput data due to their ultrahigh dimensionality as well as complex structure. In this article, we propose a new family of tensor regression models that efficiently exploit the special structure of tensor covariates. Under this framework, ultrahigh dimensionality is reduced to a manageable level, resulting in efficient estimation and prediction. A fast and highly scalable estimation algorithm is proposed for maximum likelihood estimation and its associated asymptotic properties are studied. Effectiveness of the new methods is demonstrated on both synthetic and real MRI imaging data.
|Alternate Journal||J Am Stat Assoc|
|Original Publication||Tensor regression with applications in neuroimaging data analysis.|
|PubMed Central ID||PMC4004091|
|Grant List||R01 HG006139 / HG / NHGRI NIH HHS / United States |
TL1 RR025745 / RR / NCRR NIH HHS / United States
R01 MH086633 / MH / NIMH NIH HHS / United States
UL1 RR025747 / RR / NCRR NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
KL2 RR025746 / RR / NCRR NIH HHS / United States
Tensor Regression with Applications in Neuroimaging Data Analysis.