Fall 2016
Seminars are held on Thursdays at 4:00PM. Please note the location for each event in the schedule below. For questions about the seminar schedule, please contact Antai Wang.
Date: October 6, 2016
Speaker: Dan Yang
Department of Statistics,
Rutgers University
Title: "Bilinear Regression with Matrix Covariates in High Dimensions"
Abstract:
Traditional functional linear regression usually takes a one dimensional functional predictor as input and estimates the continuous coefficient function. Modern applications often generate two dimensional covariates, which when observed at grid points are matrices. To avoid inefficiency of the classical method involving estimation of a two dimensional coefficient function, we propose a bilinear regression model and obtain estimates via a smoothness regularization method. The proposed estimator exhibits minimax optimal property for prediction under the framework of Reproducing Kernel Hilbert Space. The merits of the method are further demonstrated by numerical experiments and an application on real imaging data.