Lexin Li, of the Division of Biostatistics at the University of California – Berkeley, will present “Tensor Response Regression and Neuroimaging Analysis”.
Abstract: Classical regression models treat variables (predictor or response) as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate variables of more complex form such as multidimensional arrays (tensors). Traditional statistical and computational methods are proving insufficient for analysis of those data due to their ultrahigh dimensionality as well as complex structure. In this talk, we propose a family of tensor response regression models that efficiently exploit the special structure of tensors. Under this framework, ultrahigh dimensionality is reduced to a manageable level, resulting in efficient estimation and prediction. Fast and highly scalable estimation algorithms are proposed, and asymptotic properties are obtained. Effectiveness of the new methods is demonstrated on real neuroimaging data analysis.
A social tea will be held at 3:00 p.m. in A434 Mayo. All are welcome.