A Model-calibration Approach to Using Complete Auxiliary Information from Stratified Sampling Survey Data

在成层的调查采样，有时，我们有完全的辅助信息。基本问题之一是怎么有效地在评价阶段使用完全的辅助信息。在这份报纸，我们扩大模型刻度方法由从成层的采样调查数据使用完全的辅助信息获得有限人口平均数的评估者。我们证明产生评估者有效地在评价阶段使用辅助信息并且拥有很多个吸引人的特征例如不管工作模型并且近似 asymptotically 设计不偏在模型下面模型不偏。当一个线性工作模型被使用时，产生评估者归结为平常的刻度评估者(或 GREG ) 。

In stratified survey sampling, sometimes we have complete auxiliary information. One of the fundamental questions is how to effectively use the complete auxiliary information at the estimation stage. In this paper, we extend the model-calibration method to obtain estimators of the finite population mean by using complete auxiliary information from stratified sampling survey data. We show that the resulting estimators effectively use auxiliary information at the estimation stage and possess a number of attractive features such as asymptotically design-unbiased irrespective of the working model and approximately model-unbiased under the model. When a linear working-model is used, the resulting estimators reduce to the usual calibration estimator（or GREG）.