论文标题
在信息抽样下的半参数自适应估计
Semiparametric adaptive estimation under informative sampling
论文作者
论文摘要
在调查采样中，调查数据不一定代表目标人群，并且样本通常是偏见的。但是，有关调查权重的信息有助于消除选择偏差。 Horvitz-Thompson估计量是一种众所周知的无偏，一致且渐近正常的估计量。但是，这不是有效的。因此，本研究通过将调查权重作为随机变量来得出各种目标参数的半参数效率，因此提出了一个半参数最佳估计器，其中具有某些有关调查权重的工作模型。所提出的估计量在一类规则和渐近线性估计器中是一致的，渐近的正常和有效的。此外，进行了有限的仿真研究，以研究该方法的有限样本性能。提出的方法适用于1999年的加拿大工作场所和员工调查数据。
In survey sampling, survey data do not necessarily represent the target population, and the samples are often biased. However, information on the survey weights aids in the elimination of selection bias. The Horvitz-Thompson estimator is a well-known unbiased, consistent, and asymptotically normal estimator; however, it is not efficient. Thus, this study derives the semiparametric efficiency bound for various target parameters by considering the survey weight as a random variable and consequently proposes a semiparametric optimal estimator with certain working models on the survey weights. The proposed estimator is consistent, asymptotically normal, and efficient in a class of the regular and asymptotically linear estimators. Further, 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.
