报告题目：Corrected Akaike information criterion with general covariance matrix

报 告 人：喻达磊 教授 云南财经大学

报告时间：2021年11月12日 9:30-10:30

报告地点：腾讯会议 ID：221 156 831 会议密码：1112

校内联系人：朱复康 fzhu@jlu.edu.cn

报告摘要：In this paper, within the framework of Stein’s identity, we propose a new corrected Akaike information criterion for the finite sample setting. The new criterion applies to the situation where very general covariance structures are involved. Under certain regularity conditions, we establish the asymptotic efficiency of the proposed model selection criterion. Simulations in the spatial regression model with autoregressive errors show that our method is promising when the difference between the candidate models and the true data generating process is small. Our method becomes particularly competitive with its competitors when such difference becomes larger. The proposed model selection criterion is also applied to the analysis of a set of real data (the Neighborhood Crimes Data) and the results further support the use of our method in practical situations.

报告人简介：喻达磊，云南财经大学教授，博士生导师，在香港城市大学获得博士学位。研究领域为随机效应模型、混合模型以及空间计量模型的模型选择、模型平均和估计理论等。已在包括JRSS-B，JASA和中国科学：数学在内的国内外统计学期刊上发表论文十余篇。主持国家自然科学基金项目三项，入选了云南省中青年学术和技术带头人后备人才和云南省“万人计划”的“青年拔尖人才”专项。担任过Biometrics, Statistica Sinica, Computational Statistics & Data Analysis, Statistical Analysis and Data Mining，《系统工程理论与实践》和《系统科学与数学》等期刊的匿名审稿人。