Geographically Weighted Regression-Based Model Calibration Estimation of Finite Population Total Under Geo-referenced Complex Surveys

Bappa Saha, Ankur Biswas (), Tauqueer Ahmad and Nobin Chandra Paul
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Bappa Saha: ICAR-Indian Agricultural Statistics Research Institute
Ankur Biswas: ICAR-Indian Agricultural Statistics Research Institute
Tauqueer Ahmad: ICAR-Indian Agricultural Statistics Research Institute
Nobin Chandra Paul: ICAR-Indian Agricultural Statistics Research Institute

Journal of Agricultural, Biological and Environmental Statistics, 2024, vol. 29, issue 4, No 7, 793-811

Abstract: Abstract 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

Keywords: Geographically weighted regression; Model calibration; Spatial non-stationarity; Superpopulation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13253-023-00576-9

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