【学术报告】Identifiability and Estimation in Generalized Linear Models with Nonignorable Missing Data
报告人：邵军教授 美国威斯康辛-麦迪逊大学统计系
报告题目：Identifiability and Estimation in Generalized Linear Models with Nonignorable Missing Data
报告时间： 2014年4月2日（星期三）16:00
报告地点：数学院第三报告厅
报告摘要：We consider identifiability and estimation in a generalized linear model in which the response variable and some covariates have missing values and the missing data mechanism is nonignorable and unspecified.We adopt a pseudo likelihood approach that makes use of an instrumental variable to help identifying unknown parameters in the presence of nonignorable missing data.
Explicit conditions for the identifiability of parameters are given. Some asymptotic properties of the parameter estimators based on maximizing the pseudo likelihood are established. Explicit asymptotic covariance matrix and its estimator are also derived in some cases. For the numerical maximization of the pseudo likelihood, we develop a two-step iteration algorithm that decomposes a non-concave maximization problem into two problems of maximizing concave functions. Some simulation results and an application to a data set from cotton factory workers are also presented.
欢迎广大师生参加！
南开大学统计研究院
2014年3月25日