前沿论坛

题目：Frequentist Model Averaging for Undirected Gaussian Graphical Models

报告人：张新雨（中科院数学与系统科学研究院）

邀请人：徐平峰（东北师范大学）

时间：2024年11月29日 9：00~10：00

地点：腾讯会议（#腾讯会议： 281-842-881 ）

报告内容简介：  

Advances in information technologies have made network data increasingly frequent in a spectrum of big data applications, which is often explored by probabilistic graphical models. To precisely estimate the precision matrix, we propose an optimal model averaging estimator for Gaussian graphs (MAEGG). We prove that the proposed estimator is asymptotically optimal when candidate models are misspecified and achieves sample consistency when at least one correct model is included in the candidate set. Furthermore, numerical simulations and a real data analysis on yeast genetic data were conducted to illustrate that the proposed method is promising. (Jointly with Huihang Liu)

报告人简介：张新雨，中科院数学与系统科学研究院研究员。主要从事统计学和计量经济学的理论和应用研究工作，具体研究方向包括模型平均、机器学习和组合预测等。担任期刊《JSSC》领域主编、期刊《系统科学与数学》、《数理统计与管理》等的编委，主持优秀和杰出青年基金项目，曾获中国青年科技奖。