Semiparametric adaptive estimation under informative sampling
研究了在信息性抽样中，将抽样权重视为随机变量，推导半参数效率界，并提出了两种半参数估计量，一种基于参数工作模型，另一种使用去偏/双机器学习方法，在加拿大工作场所与雇员调查数据中验证了有效性。
In probability sampling, sampling weights are often used to remove selection bias in the sample. The Horvitz–Thompson estimator is well known to be consistent and asymptotically normally distributed; however, it is not necessarily efficient. This study derives the semiparametric efficiency bound for various target parameters by considering the survey weights as random variables and consequently proposes two semiparametric estimators with working models on the survey weights. One estimator assumes a reasonable parametric working model, but the other estimator does not require specific working models by using the debiased/double machine learning method. The proposed estimators are consistent, asymptotically normal, and efficient in a class of regular and asymptotically linear estimators. A limited simulation study is conducted to investigate the finite sample performance of the proposed method. The proposed method is applied to the 1999 Canadian Workplace and Employee Survey data.