报告题目：Homogeneity and Sparsity Analysis for High Dimensional Panel Data Models

报 告 人：朱仲义教授 博士生导师

报告时间：2022年11月15日（周二）下午16:00-19:00

腾讯会议：233 877 241

摘要：In this paper, we are interested in detecting latent group structures and significant covariates in a high-dimensional panel data model with both individual and time fixed effects. The slope coefficients of the model are assumed to be subject dependent, and there exist group structures where the slope coefficients are homogeneous within groups and heterogeneous between groups. We develop a penalized estimator for recovering the group structures and the sparsity patterns simultaneously. We propose a new algorithm to optimize the objective function. Furthermore, we propose a strategy to reduce the computational complexity by pruning the penalty terms in the objective function, which also improves the accuracy of group structure detection. The proposed estimator can recover the latent group structures and the sparsity patterns consistently in large samples. The finite sample performance of the proposed estimator is evaluated through Monte Carlo studies and illustrated with a real data set.

报告人简介：朱仲义，复旦大学统计系教授，博士生导师；曾任中国概率统计学会第八、九届副理事长，国际著名杂志《Statistica Sinica》副主编；《应用概率统计》和《数理统计与管理》杂志编委，中国统计教材编审委员会委员；现为Elected ISI Member（国际统计学会推选会员），《中国科学:数学》杂志编委。研究方向包括保险精算、纵向数据(面板数据)模型、分位数回归模型等统计推断问题研究。目前主持国家自然科学基金重大项目子项目一项，重点项目子项目一项以及面上项目一项，已主持完成国家自然科学基金四项、国家社会科学基金一项。发表学术论文100多篇，包括在国际四大统计顶级刊物等SCI论文60多篇，并获教育部自然科学二等奖一次。

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