STOR Colloquium: Lexin Li, University of California at Berkeley

Tensor Regression and Applications in Neuroimaging Analysis

Classical regression methods treat covariates (response or predictor) 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 array (tensor). Traditional statistical and computational methods are proving insufficient for analysis of such data due to their ultrahigh dimensionality as well as complex structure. In this talk, we propose a new family of tensor regression models that reduce the ultrahigh dimensionality to a manageable level, which in turn leading to efficient model estimation and prediction. Some applications to real neuroimaging data analysis will be discussed