报告时间: 2021年10月28日 上午9:30-10:30
报告地点: 腾讯会议（会议ID：352 846 507）
报告嘉宾: 李伟
报告主题: 完整数据分布不可识别下识别和估计不可忽略缺失结果的均值
报告摘要: We consider the problem of making inference about the population outcome mean of an outcome variable subject to nonignorable missingness. By leveraging a so-called shadow variable for the outcome, we propose a novel condition that ensures nonparametric identification of the outcome mean, although the full data distribution is not identified. The identifying condition requires the existence of a function as a solution to a representer equation that connects the shadow variable to the outcome mean. Under this condition, we use sieves to nonparametrically solve the representer equation and propose an estimator which avoids modeling the propensity score or the outcome regression. We establish the asymptotic properties of the proposed estimator. We also show that the estimator is locally efficient and attains the semiparametric efficiency bound for the shadow variable model under certain regularity conditions. We illustrate the proposed approach via simulations and a real data application on home pricing. The paper is available at http://arxiv.org/abs/2110.05776.
个人简介: 李伟，中国人民大学统计学院，生物统计与流行病学系讲师，北京大学数学科学学院博士。主要研究领域为因果推断、缺失数据、高维统计等。目前已在包括Biometrika, Journal of Econometrics, Biometrics等国际著名统计期刊上发表多篇学术论文。主持一项国家自然科学青年基金项目，参与完成多项国家自然科学基金面上项目。
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