报告人：Shan Yu (University of Virginia)

时间：2021年11月4日星期四9:00-10:00

腾讯会议ID：182 204 896

摘要：

In this talk, the speaker will present a sparse multi-group functional linear regression model to simultaneously estimate multiple coefficient functions and identify groups, such that coefficient functions are identical within groups and distinct across groups. By borrowing information from relevant subgroups of subjects, the proposed method enhances estimation efficiency while preserving heterogeneity in model parameters and coefficient functions. An adaptive fused lasso penalty is used to shrink coefficient estimates to a common value within each group. The speaker will also present theoretical properties of the proposed estimators. To enhance computation efficiency and incorporate neighborhood information, we propose to use graph-constrained adaptive lasso with a highly efficient algorithm. Two Monte Carlo simulation studies have been conducted to study the finite-sample performance of the proposed method. The proposed method is applied to sorghum flowering-time data and hybrid maize grain yields from the Genomes to Fields consortium.

报告人简介：

Dr. Shan Yu joined the Department of Statistics at the University of Virginia as an Assistant Professor last August 2020 after receiving her Ph.D. from Iowa State University. Her research interests focus on advanced statistical methods for complex-structured data, statistical machine learning, and big data analytics. Specifically, she has been engaged in projects utilizing non-/semi-parametric regression methods, spatial/spatiotemporal data analysis, biomedical imaging analysis, statistical epidemiology, and trajectory data analysis.

邀请人：张园园