报告时间：2020 / 07 / 01（周三）14:00
报告形式：腾讯会议
报告嘉宾：周静
报告主题：Imputation for Spatial Dynamic Panel Data with Dependent Variable Missing at Random
报告摘要：Missing data is a common problem that researchers face in practice. In this article, we focus on the missing response problem for a spatial dynamic panel data (SDPD) model, which allows for both spatial and temporal dependencies. A logistic regression with a set of pre-specified covariates is used to model the missingness mechanism, which is assumed to be missing at random (MAR). A weighted maximum likelihood estimator (WMLE) is proposed for parameter estimation in the presence of incomplete data. The associated asymptotic properties are investigated. Thereafter, we develop a novel imputation method, which makes use of the information from spatial dependence, temporal dependence, and exogenous regression covariates. Lastly, the performance of WMLE and the proposed imputation method are demonstrated by both simulation studies and a real data example.
个人简介：周静，中国人民大学统计学院助理教授，北京大学光华管理学院管理学博士，研究上关注复杂网络数据建模、营销模型、消费者行为分析等，研究论文发表于 Journal of Business and Economic Statistics、Science China Mathematics、Statistics and its Interface、Statistica Sinica、管理科学，营销科学学报等国内外权威杂志上。主持国家自然科学基金、北京市社会基金等多项省部级以上课题。