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伟德BETVlCTOR1946源于英国学术沙龙(2022-2023学年第七期)
作者: 日期:2022-12-05 点击量:

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)1330-1430

地 点:6号学院楼510会议室

主办单位:伟德BETVlCTOR1946源于英国 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。现任伟德BETVlCTOR1946源于英国应用统计系副教授,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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