A0777
Title: Functional calibration under non-probability survey sampling
Authors: Zhonglei Wang - Xiamen University (China) [presenting]
Xiaojun Mao - Shanghai Jiao Tong University (China)
Jae Kwang Kim - Iowa State University (United States)
Abstract: Non-probability sampling is prevailing in survey sampling, but ignoring its selection bias leads to erroneous inferences. Incorporating auxiliary information from an independent probability sample, we propose a unified nonparametric method to estimate the sampling weights for a non-probability sample by calibrating functions of auxiliaries in a reproducing kernel Hilbert space. The consistency and the limiting distribution of the proposed estimator are established under rejective sampling, and the corresponding variance estimator is also investigated. Compared with existing works, the proposed method is more robust since no parametric assumption is needed 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 probability sample from the Korea National Health and Nutrition Examination Survey.
