报告时间： 2019年12月12日周四下午14:00~15:00
报告地点： X2511
报告题目:
Regression Analysis with individual-specific patterns of missing covariates *
Abstract:
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.
*Joint work with Wei Liu, Wei Lan.
报告人简介：林华珍，西南财经大学统计学院教授、博导，统计研究中心主任，美国华盛顿老员工物统计系博士后，四川大学博士。教育部长江学者特聘教授，国家百千万人才工程入选者，国家杰出青年科学基金获得者，教育部新世纪优秀人才， 第十一批四川省学术和技术带头人，第十批成都市有突出贡献的优秀专家，2019年首批“四川省教书育人名师”。 有多篇学术论文发表在AoS、JASA、JoE、JRSSB、Biometrika及Biometrcs等国际统计学和计量经济学顶级期刊上， 先后是国际统计学期刊《Biometrics》、《Scandinavian Journal of Statistics》、《Statistics and Its Interface》、《Statistical Theory and Related Fields》Associate Editor， 国内核心学术期刊《应用概率统计》、《系统科学与数学》、《数理统计与管理》编委。
