报告时间：本周四（6月2号）下午3:00-4:00
报告地点：统计楼103
报告人：栗家量，新加坡国立大学统计与应用概率系和Duke-NUS医学研究生院的副教授，新加坡眼科研究中心的研究员。
研究领域主要涉及非参数统计、纵向数据、高纬数据、生存分析、医学诊断，在Annals of statistics,JASA,JRSSB, Biometrics,Biostatistics, Statistics in Medicine等杂志已发表109篇论文。现为Biometrics杂志和Lifetime Data Analysis杂志副主编，以及Biomarkers杂志的统计主编。其研究和教学成果获得了多项科研奖励，包括新加坡医学研究理事会的新研究员资助奖和合作基础研究资助奖，以及新加坡国立大学的青年科学家奖。
报告题目：STRUCTURE IDENTIFICATION IN PANEL DATA ANALYSIS
报告摘要：Panel data analysis is an important topic in statistics and econometrics. In such analysis, it is very common to assume the impact of a covariate on the response variable remains constant across all individuals. Whilst the modelling based on this assumption is reasonable when only the global effect is of interest, in general, it may overlook some individual/subgroup attributes of the true covariate impact. In this paper, we propose a data driven approach to identify the groups in panel data with interactive effects induced by latent variables. It is assumed that the impact of a covariate is the same within each group, but different between the groups. An EM based algorithm is proposed to estimate the unknown parameters, and a binary segmentation based algorithm is proposed to detect the grouping. We then establish asymptotic theories to justify the proposed estimation, grouping method, and the modelling idea. Simulation studies are also conducted to compare the proposed method with the existing approaches, and the results obtained favour our method. Finally, the proposed method is applied to analyse a data set about income dynamics, which leads to some interesting findings.