题目：Regression Analysis with individual-specific patterns of missing covariates
报告人：林华珍教授, 西南财经大学
报告时间：2020年10月30日下午3:00-4:00
报告地点：数理楼362室
摘要：It is increasingly common to collect data from heterogeneous sources in practice. Two major challenges complicate the statistical analysis of such data. First, only a small proportion of units have complete information across all sources. Second, the missing data patterns vary across individuals. Our motivating online-loan data have 93% missing covariates where the missing pattern is individual-specific. The existing regression analysis with missing covariates either are inefficient or require additional modeling assumptions on the covariates. We propose a simple yet efficient iterative least squares estimator of the regression coefficient for the data with individual-specific missing patterns. Our method has several desirable features. First, it does not require any modeling assumptions on the covariates. Second, the imputation of the missing covariates involves feasible one-dimensional nonparametric regressions, and can maximally use the information across units and the relationship among the covariates. Third, the iterative least squares estimate is both computationally and statistically efficient. We study the asymptotic properties of our estimator and apply it to the motivating online-loan data.
林华珍教授简介：西南财经大学统计学院教授、博士生导师;国际概率论与数理统计学会中国分会(IMS-China)委员(08-10年，10-12年);《应用概率统计》第七届编委;第九届中国概率统计学会理事。曾在四川大学数学系获学士、硕士学位，华西医科大学公共卫生学院获博士学位，美国华盛顿大学生物统计系博士后。国家杰出青年科学基金获得者，教育部特聘教授。主要从事非参数统计理论及应用研究，先后有论文发表在Annals of Statistics、JRSSB、Biometrika及Biometrics等国际统计学顶级期刊上。已先后4次主持国家自然科学基金项目。林华珍获教育部新世纪优秀人才支持计划。2015年荣获第十一批四川省学术和技术带头人。担任《Biometrics》，《Statistics and Its Interface》《Scandinavian Journal of Statistics》Associate Editor，同时也是国内重要学术期刊《数理统计与管理》、《应用概率统计》及《系统科学与数学》的编委。林华珍教授在非参数理论和方法，转换模型，相关数据分析等领域获得巨大成就。她申请的项目是全国财经院校中首次获得国家杰出青年科学基金立项，也是全国统计学界第五次获得该类项目的资助。