In sample surveys, the model calibration approach is an improvement over the usual calibration approach, where the concept of the calibration approach is generalized to obtain a model-assisted estimator using more complex models based on complete auxiliary information. In many surveys, the study and auxiliary variables vary across locations and the observations tend to be similar for the nearby units than those located further apart. In such situations, a simple global model cannot explain the relationships between some sets of variables. This phenomenon is known as spatial non-stationarity which is considered by the geographically weighted regression (GWR) model. It can capture the spatially varying relationship between different variables. In the present study, GWR-based model calibration estimators of population total of the study variable were developed in the context of geo-referenced complex survey designs when complete auxiliary information along with their spatial locations is available at population level. The asymptotic properties of the developed GWR-based model calibration estimators were evaluated under a set of assumptions. Under the same set of assumptions, the variances and estimators of variances of the developed estimators were given. Through a spatial simulation study, the performance of the developed estimators was compared to that of existing estimators and found to be more efficient than the existing ones. Supplementary materials accompanying this paper appear online

在抽样调查中，模型校准方法是对通常校准方法的改进，其中校准方法的概念被推广，以基于完整的辅助信息使用更复杂的模型来获得模型辅助估计器。在许多调查中，研究变量和辅助变量因地点而异，并且附近单位的观察结果往往与相距较远的单位相似。在这种情况下，简单的全局模型无法解释某些变量集之间的关系。这种现象称为空间非平稳性，由地理加权回归（GWR）模型考虑。它可以捕获不同变量之间的空间变化关系。在本研究中，当完整的辅助信息及其空间位置在人口水平上可用时，研究变量的人口总数的基于 GWR 的模型校准估计器是在地理参考复杂调查设计的背景下开发的。所开发的基于 GWR 的模型校准估计器的渐近特性在一组假设下进行了评估。在同一组假设下，给出了所开发的估计量的方差和方差估计量。通过空间模拟研究，将所开发的估计器的性能与现有估计器的性能进行了比较，发现比现有估计器更有效。本文附带的补充材料出现在网上