我校统计与数学学院喻达磊副教授与中国科学院数学与系统科学研究院张新雨副研究员和香港城市大学邱启荣教授合作撰写的论文Asymptotic properties and information criteria for misspecified generalized linear mixed models于2018年2月在统计学国际顶级期刊Journal of the Royal Statistical Society, Series B(Statistical Methodology)上在线发表（相关链接：http://onlinelibrary.wiley.com/doi/10.1111/rssb.12270/full），该论文第一作者为喻达磊副教授，通讯作者为张新雨副研究员。Journal of the Royal Statistical Society, Series B(Statistical Methodology)和The Annals of Statistics，Journal of the American Statistical Association以及Biometrika是统计学领域国际公认的最好的4本学术期刊。
论文摘要：
The problem of misspecification poses challenges in model selection. The paper studies the asymptotic properties of estimators for generalized linear mixed models with misspecification under the framework of conditional Kullback–Leibler divergence. A conditional generalized information criterion is introduced, and a model selection procedure is proposed by minimizing the criterion. We prove that the model selection procedure proposed is asymptotically loss efficient when all the candidate models are misspecified. The model selection consistency of the model selection procedure is also established when the true data-generating procedure lies within the set of candidate models. Simulation experiments confirm the effectiveness of the method proposed. The use of the criterion for model selection is illustrated through an analysis of the European Currency Opinion Survey data.
喻达磊老师简介：
喻达磊老师博士毕业于香港城市大学管理科学系，现为统计与数学学院副教授。主持国家级课题两项、云南省科技计划一项。主要从事模型选择、模型平均和随机效应模型的研究。在国际顶级统计学期刊Journal of the Royal Statistical Society: Series B (Statistical Methodology) 和Journal of the American Statistical Association以及国际统计学期刊Scandinavian Journal of Statistics，Statistics in Medicine， Journal of Multivariate Analysis，Computational Statistics & Data Analysis上发表论文多篇。
