论文标题
在非概率调查采样下的功能校准
Functional Calibration under Non-Probability Survey Sampling
论文作者
论文摘要
非概率抽样在调查采样中占上风，但忽略其选择偏见会导致错误的推论。我们提供了一种统一的非参数校准方法，可以通过在繁殖的内核希尔伯特空间中校准辅助变量的辅助变量来估算非概率样本的采样权重。建立了所提出的估计器的一致性和限制分布，还研究了相应的方差估计器。与现有作品相比，由于没有针对非概率样本的选择机理做出参数假设，因此提出的方法更加可靠。数值结果表明，所提出的方法的表现优于其竞争对手，尤其是当模型被弄清楚时。提出的方法用于根据国家健康保险共享服务的非概率样本以及韩国国家健康和营养检查调查的参考概率样本来分析韩国公民的平均胆固醇。
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.
