报告题目：Homogeneity Pursuit in Single Index Models based Panel Data Analysis
报告人：张文扬 教授 The University of York
邀请人：刘三阳 教授、杨丹丹 副教授
报告时间：2018年3月26日9：30-10:30
报告地点：信远楼II206数统院报告厅

报告人简介：
张文扬，英国约克大学统计学首席教授、英国皇家统计学会科学委员会委员，现担任统计学三大顶级期刊之一《Journal of the American Statistical Association》的副主编，曾任IMS Lecture Notes - Monograph Series的编委；在统计学的国际顶级期刊上发表学术论文多篇。
报告摘要：Panel data analysis is an important topic in statistics and econometrics.  Traditionally, in panel data analysis, all individuals are assumed to share the same unknown parameters, e.g. the same coefficients of covariates when the linear models are used, and the differences between the individuals are accounted for by cluster effects.  This kind of modeling only makes sense if our main interest is on the global trend, this is because it would not be able to tell us anything about the individual attributes which are sometimes very important.  In this talk, I will present a new modeling approach, based on the single index models embedded with homogeneity, for panel data analysis, which builds the individual attributes in the model and is parsimonious at the same time.  I will show a data driven approach to identify the structure of homogeneity, and estimate the unknown parameters and functions based on the identified structure.  I will show the asymptotic properties of the resulting estimators.  I will also use intensive simulation studies to show how well the resulting estimators work when sample size is finite.  Finally, I will apply the proposed modeling idea to a public financial dataset and a UK climate dataset, and show some interesting findings.
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