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

Author

Listed:
  • Bappa Saha

    (ICAR-Indian Agricultural Statistics Research Institute
    ICAR-Indian Agricultural 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)

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

Suggested Citation

  • Bappa Saha & Ankur Biswas & Tauqueer Ahmad & Nobin Chandra Paul, 2024. "Geographically Weighted Regression-Based Model Calibration Estimation of Finite Population Total Under Geo-referenced Complex Surveys," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(4), pages 793-811, December.
    • Handle: RePEc:spr:jagbes:v:29:y:2024:i:4:d:10.1007_s13253-023-00576-9
    DOI: 10.1007/s13253-023-00576-9
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    References listed on IDEAS

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    1. Wu C. & Sitter R. R, 2001. "A Model-Calibration Approach to Using Complete Auxiliary Information From Survey Data," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 185-193, March.
    2. Ankur Biswas & Anil Rai & Tauqueer Ahmad & Prachi Misra Sahoo, 2017. "Spatial estimation and rescaled spatial bootstrap approach for finite population," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(1), pages 373-388, January.
    3. Chao Liu & Chuanhua Wei & Yunan Su, 2018. "Geographically weighted regression model-assisted estimation in survey sampling," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(4), pages 906-925, October.
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