題 目：Robust Variable Selection via Nonconcave Penalties with a Upgraded Parsimonious Dynamic Covariance Modeling
主講人：許林副教授
時 間：2022年12月6日（周二）13：30-14：30
地 點：6號學院樓510會議室
主辦單位：新葡萄8883官網AMG 浙江省2011“數據科學與大數據分析協同創新中心”
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.
主講人簡介：
許林，博士畢業于東北師范大學數學與統計學院，并于2016-2018年在加州大學河濱分校統計系進行博士后研究。現任新葡萄8883官網AMG應用統計系副教授，碩士研究生導師；專業研究方向為：縱向數據分析；穩健估計；因果推斷；經驗似然理論等。主持完成省部級科學基金兩項。近幾年在JMVA、SII、CSDA等統計學雜志發表論文10篇。
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