时 间：2019年3月14日(周四)下午15:00-16:00
地 点：中北校区理科大楼A1716报告厅
题 目：Semi-parametric Density Models
报告人：Yuedong Wang University of California-Santa Barbara
摘 要：
Maximum likelihood estimation within a parametric family and nonparametric estimation are two traditional approaches for density estimation. Sometimes it is advantageous to model some components of the density function parametrically while leaving other components unspecified. We propose estimation methods for a general semiparametric density model and develop computational procedures under different situations. We also present simulation results and real data examples.
报告人简介：
Yuedong Wang, Professor, Fellow of ASA and ISI, main research direction includes smoothing spline, mixed-effects models, survival analysis, and longitudinal data. He obtained PhD degree in Statistics from the University of Wisconsin in 1994. Current a professor and former chair of the Department of Statistics and Applied Probability at the University of California-Santa Barbara. He has published over 100 papers. He is currently an editor in chief of the Statistics and Its Interface.