学术报告通知

报告题目：Generalized ℓ1-penalized quantile regression with linear constraints

报告人：刘永欣 博士（ 南京审计大学  统计与数学学院）

报告时间：2019年12月23日  9:00—10:00

报告地点：37000cm威尼斯 B410

报告摘要：This talk consider a generalized ℓ1-penalized quantile regression with linear constraints on parameters, including either linear inequality or equality constraints or both. It allows a general form of penalization, including the usual lasso, the fused lasso and the adaptive lasso as special cases. The KKT conditions of the optimization problem are derived and the whole solution path is computed as a function of the tuning parameter. A formula for the number of degrees of freedom is derived, which is used to construct model selection criteria for selecting optimal tuning parameters. Finally, several simulation studies and two real data examples are presented to illustrate the proposed method.

报告人简介：

刘永欣，2019年于37000cm威尼斯获理学博士学位，2016年至2017年间美国奥本大学访学学者，现为南京审计大学统计与数学学院青年教师。研究方向：高维变量选择、稳健统计及大数据的统计分析等。在Computational Statistics and Data Analysis, Journal of Statistical Computation and Simulation等统计学国际知名学术期刊发表论文多篇。