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Identification and Estimation of generalized linear models with parametric nonignorable missing data mechanism

发布时间:2019-03-26     来源:    点击数:
主题: Identification and Estimation of generalized linear models with parametric nonignorable missing data mechanism
类型: 学术报告
主办方:
报告人: 崔霞 教授 (广州大学)
日期: 2015年12月15日,15:00-16:00
地点: 知新楼B1238
内容:

报 告 人:崔霞 教授 (广州大学)

报告题目:Identification and Estimation of generalized linear models with parametric nonignorable missing data mechanism

报告时间:2015年12月15日,15:00-16:00

报告地点:知新楼B1238

摘要:We address the problem of identifying and estimating generalized linear models when the response values are nonignorably missing. A Logistic/Probit/Log-log pattern
is taken to specify the missing data mechanism.In this situation, likelihood based on observed data may not be identifiable. In this article, we prove the models parameters are identifiable under very mild conditions and then construct estimators based on a likelihood-based approach. The proposed estimators are shown to be consistent and asymptotically normal. Simulation studies demonstrate that the proposed inference procedure performs well in many settings. We apply the proposed method to a data set from research in environmental study.

 

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