讲座题目：A superpopulation treatment to case-control data analysis

主讲人：Yanyuan Ma教授  宾夕法尼亚州立大学

讲座时间：2019年5月24日(周五）下午14:00-15:00

讲座地点：综合楼615

主讲人简介：Yanyuan Ma教授1999年麻省理工获得应用数学博士学位，先后在南Carolina大学及德克萨斯农工大学任统计学教授，现为宾夕法尼亚州立大学统计学教授。是 IMS与ASA会士，是JRSSB等一些国际顶级期刊的编委， 是中组部千人。她在降维、潜变量方法、测量误差数据分析、生存分析及半参数统计等方面作出了很多杰出的工作，在国际重要期刊发表论文百余篇，其中40余篇发表在顶级期刊。

讲座摘要：We study the regression relationship among covariates in case-control data, an area known as the secondary analysis of case-control studies. The context is such that only the form of the regression mean is specified, so that we allow an arbitrary regression error distribution, which can depend on the covariates and thus can be heteroscedastic. Under mild regularity conditions we establish the theoretical identifiability of such models. Previous work in this context has either (a) specified a fully parametric distribution for the regression errors, (b) specified a homoscedastic distribution for the regression errors, (c) has specified the rate of disease in the population (we refer this as true population), or (d) has made a rare disease approximation. We construct a class of semiparametric estimation procedures that rely on none of these. The estimators differ from the usual semiparametric ones in that they draw conclusions about the true population, while technically operating in a hypothetic superpopulation. We also construct estimators with a unique feature, in that they are robust against the misspecification of the regression error distribution in terms of variance structure, while all other nonparametric effects are estimated despite of the biased samples. We establish the asymptotic properties of the estimators and illustrate their finite sample performance through simulation studies.