题 目：Dynamic weak-significant variable selection of error component model

via robust MM algorithm

主讲人：许林副教授

时 间：2022年3月10日（周四）13：30-14：30

地 点：6号学院楼500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：

The penalized-likelihood based variable selection methods rely heavily on fixed thresholding functions to carry out static variable selection, and as a result, weak-significant variables (i.e., variables that are deemed important, but whose regression coefficients are small in absolute values) are often kicked out completely. In addition, the tuning parameters of these methods are usually selected by cross-validation, which only use the average information of partial data. In this paper, based on MM algorithm, we propose a dynamic threshold function for variable selection, which use the information of the complete dataset and can retain important variables with weak signal. The methodology is applied to panel data with random effects, and a two-step estimation procedure is proposed. We show that the new majorizing function has the same convergence property as the original one, and the performance of the two functions are compared numerically. Numerical studies show that when error distributions are heavy-tailed or skewed, our methods work better than existing variable selection techniques, especially in keeping important variables with weak signal.

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完成2年期博士后研究，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8篇。

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