| Annie (Peiyong) Qu, University of Illinois, Urbana-Champaign |
| Title: Time-varying networks estimation and dynamic model selection |
| Abstract: In many biomedical and social science studies, it is very important to identify and predict the dynamic changes of associations among network data over time. We propose a varying-coefficient model to incorporate time-varying network data, and impose a piecewise penalty function to capture local features of the network associations. The advantages of the proposed approach are that it is nonparametric and therefore flexible in modeling dynamic changes of association for network data problems, and capable of identifying the time regions when dynamic changes of associations occur. To achieve local sparsity of network estimation, we implement a group penalization strategy involving overlapping parameters among different groups. However, this imposes great challenges in the optimization process for handling large-dimensional network data observed at many time points. We develop a fast algorithm, based on the smoothing proximal gradient method, which is computationally efficient and accurate. We illustrate the proposed method through simulation studies and children’s attention deficit hyperactivity disorder fMRI data, and show that the proposed method and algorithm efficiently recover the dynamic network changes over time. The proposed approach works especially well when networks are sparse. This is joint work with Xinxin Shu. |
Friday, Nov 1 at 2:00pm in the conference room
Categories: Past Talks