讲座题目：Sufficient Dimension Reduction for Nonignorable Nonresponse 主讲人：王启华 研究员 主持人：张日权 教授 开始时间：2019-10-15 13:00:00 讲座地址🙋🏽：中北校区理科大楼A1716 主办单位：统计学院
报告人简介： 王启华，中国科学院核心骨干特聘研究员，博士生导师，国家杰出青年基金获得者，中科院“百人计划”入选者🦂，首届全国优秀博士论文作者，国际统计研究会当选会员（elected member）, 先后访问加拿大Carleton大学🧑🔧、美国California大学戴维斯分校、美国California大学洛杉矶分校🛖、美国Yale大学💗、美国华盛顿大学😠、美国西北大学🐦、德国Humboldt大学、澳大利亚国立大学及澳大利亚悉尼大学等。主要从事生存分析、缺失数据分析🧑🏽🏭、高维数据统计分析及非-半参数统计推断等方面的研究🤣。出版专著两部🧏🏼👱🏽♂️，在 The Annals of Statistics☝️、JASA及Biometrika等国际重要刊物发表论文百余篇，是一些国际与国内刊物的编委。 报告内容： Sufficient dimension reduction (SDR) for nonignorable nonresponse poses a challenge and thus there is still no article on this problem. In the nonignorable case, methods derived under ignorable missing assumption are invalid and of serious estimation bias, especially when missing rate is high. In this article, a regression calibration based cumulative mean estimation (RC-CUME) procedure is proposed to recover central subspace $\mathcal{S}_{Y|\mathbf{X}}$ with the help of a surrogate subspace. Asymptotic properties of RC-CUME are also investigated. To guide practical application, we construct two feasible surrogate subspaces and compare the proposed RC-CUME based on the two surrogate subspaces. A modified BIC-type criterion is adopted to determine the structural dimension of $\mathcal{S}_{y|\mathbf{X}}$. In addition, we extent our procedure to other SDR methods. Simulation studies are carried out to access the finite-sample performances of the proposed RC-CUME approach. A real data analysis is used to illustrate our method. |
