报告题目：Functional calibration under non-probability survey sampling
报 告 人：王中雷（厦门大学）
报告时间：2022年4月28日14:00-15:00
报告地址：腾讯会议：814-261-832
摘要：Non-probability sampling is prevailing in survey sampling, but ignoring its selection bias leads to erroneous inferences. We offer a unified nonparametric calibration method to estimate the sampling weights for a non-probability sample by calibrating functions of auxiliary variables in a reproducing kernel Hilbert space. The consistency and the limiting distribution of the proposed estimator are established, and the corresponding variance estimator is also investigated. Compared with existing works, the proposed method is more robust since no parametric assumption is made for the selection mechanism of the non-probability sample. Numerical results demonstrate that the proposed method outperforms its competitors, especially when the model is misspecified. The proposed method is applied to analyze the average total cholesterol of Korean citizens based on a non-probability sample from the National Health Insurance Sharing Service and a reference probability sample from the Korea National Health and Nutrition Examination Survey.
个人简介：王中雷，2018年获爱荷华州立大学获统计学博士学位，现为厦门大学王亚南经济研究院助理教授。王中雷的研究兴趣为抽样调查以及重抽样方法，其研究成果发表于JRSS-B，Biometrika等统计学期刊。
