题 目：Robust Variable Selection via Nonconcave Penalties with a Upgraded Parsimonious Dynamic Covariance Modeling

主讲人：许林副教授

时 间：2022年12月6日（周二）13：30-14：30

地 点：6号学院楼510会议室

主办单位：银河7163官网 浙江省2011“数据科学与大数据分析协同创新中心”

摘要：

We present a new parsimonious method for joint mean-covariance modeling based on M-estimation and nonconcave penalty. In this paper, the robustness of the proposed model was aimed to address the issue when the working matrix is misspecified and a spot of outliers exist in the dataset. The proposed approach outperforms the traditional method in robustness and variable selections for longitudinal data analysis, particularly when the dataset contains a spot of outliers. The simulation results back up the theoretical findings, and the methodology is further illustrated via an analysis of a real progesterone data example.

主讲人简介：

许林，博士毕业于东北师范大学数学与统计学院，并于2016-2018年在加州大学河滨分校统计系进行博士后研究。现任银河7163官网应用统计系副教授，硕士研究生导师；专业研究方向为：纵向数据分析；稳健估计；因果推断；经验似然理论等。主持完成省部级科学基金两项。近几年在JMVA、SII、CSDA等统计学杂志发表论文10篇。

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