IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v29y2024i4d10.1007_s13253-023-00576-9.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-023-00576-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13253-023-00576-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Debashis Ghosh & Michael S. Sabel, 2022. "A Weighted Sample Framework to Incorporate External Calculators for Risk Modeling," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 363-379, December.
    2. Domingo Morales & María del Mar Rueda & Dolores Esteban, 2018. "Model-Assisted Estimation of Small Area Poverty Measures: An Application within the Valencia Region in Spain," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(3), pages 873-900, August.
    3. Ieva Burakauskaitė & Andrius Čiginas, 2023. "An Approach to Integrating a Non-Probability Sample in the Population Census," Mathematics, MDPI, vol. 11(8), pages 1-14, April.
    4. M. Rueda & I. Sánchez-Borrego & A. Arcos & S. Martínez, 2010. "Model-calibration estimation of the distribution function using nonparametric regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 71(1), pages 33-44, January.
    5. Denis Devaud & Yves Tillé, 2019. "Deville and Särndal’s calibration: revisiting a 25-years-old successful optimization problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1033-1065, December.
    6. A. Arcos & M. Rueda & M. Martínez-Miranda, 2005. "Using multiparametric auxiliary information at the estimation stage," Statistical Papers, Springer, vol. 46(3), pages 339-358, July.
    7. Barranco-Chamorro, I. & Jiménez-Gamero, M.D. & Moreno-Rebollo, J.L. & Muñoz-Pichardo, J.M., 2012. "Case-deletion type diagnostics for calibration estimators in survey sampling," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2219-2236.
    8. Kim, Jong-Min & Sungur, Engin A. & Heo, Tae-Young, 2007. "Calibration approach estimators in stratified sampling," Statistics & Probability Letters, Elsevier, vol. 77(1), pages 99-103, January.
    9. J. N. K. Rao, 2021. "On Making Valid Inferences by Integrating Data from Surveys and Other Sources," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 242-272, May.
    10. Jan Pablo Burgard & Ralf Münnich & Martin Rupp, 2019. "A Generalized Calibration Approach Ensuring Coherent Estimates with Small Area Constraints," Research Papers in Economics 2019-10, University of Trier, Department of Economics.
    11. Sarjinder Singh, 2006. "Survey statisticians celebrate golden jubilee year 2003 of the linear regression estimator," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 63(1), pages 1-18, February.
    12. Yei Eun Shin & Ruth M. Pfeiffer & Barry I. Graubard & Mitchell H. Gail, 2022. "Weight calibration to improve efficiency for estimating pure risks from the additive hazards model with the nested case‐control design," Biometrics, The International Biometric Society, vol. 78(1), pages 179-191, March.
    13. Changbao Wu & Shixiao Zhang, 2019. "Comments on: Deville and Särndal’s calibration: revisiting a 25 years old successful optimization problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1082-1086, December.
    14. Dong Liang & Genevieve Nesslage & Michael Wilberg & Thomas Miller, 2017. "Bayesian Calibration of Blue Crab (Callinectes sapidus) Abundance Indices Based on Probability Surveys," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 481-497, December.
    15. Stearns, Matthew & Singh, Sarjinder, 2008. "On the estimation of the general parameter," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4253-4271, May.
    16. John Robert Warren & Andrew Halpern-Manners, 2012. "Panel Conditioning in Longitudinal Social Science Surveys," Sociological Methods & Research, , vol. 41(4), pages 491-534, November.
    17. J. A. Mayor-Gallego & J. L. Moreno-Rebollo & M. D. Jiménez-Gamero, 2019. "Estimation of the finite population distribution function using a global penalized calibration method," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(1), pages 1-35, March.
    18. Eli Ben-Michael & Avi Feller & Jesse Rothstein, 2021. "The Augmented Synthetic Control Method," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1789-1803, October.
    19. Ashutosh & Piyush Kant Rai & Ajeet Kumar Singh, 2023. "A Comparative Study on Calibration Approach Based Estimators for Domain Estimation Utilizing Power Function: Revisited," Annals of Data Science, Springer, vol. 10(6), pages 1559-1569, December.
    20. Aylin Alkaya & H. Öztaş Ayhan & Alptekin Esin, 2017. "Sequential Data Weighting Procedures For Combined Ratio Estimators In Complex Sample Surveys," Statistics in Transition New Series, Polish Statistical Association, vol. 18(2), pages 247-270, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jagbes:v:29:y:2024:i:4:d:10.1007_s13253-023-00576-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.