Functional calibration under non-probability survey sampling-王中雷助理教授(厦门大学王亚南经济研究院)
报告题目:Functional calibration under non-probability survey sampling
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，美国爱荷华州立大学统计学博士。现任厦门大学王亚南经济研究院、经济学院统计学与数据科学系助理教授。其研究领域为抽样调查、空间统计等。其论文发表在JRSS-B、Biometrika、Nature Communications以及Journal of Hydrology 等学术期刊上。