IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v227y2025icp272-281.html
   My bibliography  Save this article

A new technique for handling non-probability samples based on model-assisted kernel weighting

Author

Listed:
  • Cobo, Beatriz
  • Rueda-Sánchez, Jorge Luis
  • Ferri-García, Ramón
  • Rueda, María del Mar

Abstract

Surveys are going through massive changes, and the most important innovation is the use of non-probability samples. Non-probability samples are increasingly used for their low research costs and the speed of the attainment of results, but these surveys are expected to have strong selection bias caused by several mechanisms that can eventually lead to unreliable estimates of the population parameters of interest. Thus, the classical methods of statistical inference do not apply because the probabilities of inclusion in the sample for individual members of the population are not known. Therefore, in the last few decades, new possibilities of inference from non-probability sources have appeared.

Suggested Citation

  • Cobo, Beatriz & Rueda-Sánchez, Jorge Luis & Ferri-García, Ramón & Rueda, María del Mar, 2025. "A new technique for handling non-probability samples based on model-assisted kernel weighting," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 227(C), pages 272-281.
    • Handle: RePEc:eee:matcom:v:227:y:2025:i:c:p:272-281
    DOI: 10.1016/j.matcom.2024.08.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475424003094
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2024.08.009?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. Yilin Chen & Pengfei Li & Changbao Wu, 2020. "Doubly Robust Inference With Nonprobability Survey Samples," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 2011-2021, December.
    2. Bart Buelens & Joep Burger & Jan A. van den Brakel, 2018. "Comparing Inference Methods for Non‐probability Samples," International Statistical Review, International Statistical Institute, vol. 86(2), pages 322-343, August.
    3. Lingxiao Wang & Barry I. Graubard & Hormuzd A. Katki & and Yan Li, 2020. "Improving external validity of epidemiologic cohort analyses: a kernel weighting approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1293-1311, June.
    4. Jae Kwang Kim & Zhonglei Wang, 2019. "Sampling Techniques for Big Data Analysis," International Statistical Review, International Statistical Institute, vol. 87(S1), pages 177-191, May.
    5. Luis Castro-Martín & María del Mar Rueda & Ramón Ferri-García & César Hernando-Tamayo, 2021. "On the Use of Gradient Boosting Methods to Improve the Estimation with Data Obtained with Self-Selection Procedures," Mathematics, MDPI, vol. 9(23), pages 1-23, November.
    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. María del Mar Rueda & Sergio Martínez-Puertas & Luis Castro-Martín, 2022. "Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles," Mathematics, MDPI, vol. 10(24), pages 1-19, December.
    2. Luis Castro-Martín & María del Mar Rueda & Ramón Ferri-García & César Hernando-Tamayo, 2021. "On the Use of Gradient Boosting Methods to Improve the Estimation with Data Obtained with Self-Selection Procedures," Mathematics, MDPI, vol. 9(23), pages 1-23, November.
    3. Ferri-García, Ramón & Rueda-Sánchez, Jorge L. & Rueda, María del Mar & Cobo, Beatriz, 2024. "Estimating response propensities in nonprobability surveys using machine learning weighted models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 225(C), pages 779-793.
    4. Garcia Maria del Mar Rueda, 2023. "Book Review: Silvia Biffignandi and Jelke Bethlehem. Handbook of Web Surveys, 2nd edition. 2021 Wiley, ISBN: 978-1-119-37168-7, 624 pps," Journal of Official Statistics, Sciendo, vol. 39(4), pages 591-595, December.
    5. Chien-Min Huang & F. Jay Breidt, 2023. "A dual-frame approach for estimation with respondent-driven samples," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 65-81, April.
    6. Ramón Ferri-García & María del Mar Rueda, 2022. "Variable selection in Propensity Score Adjustment to mitigate selection bias in online surveys," Statistical Papers, Springer, vol. 63(6), pages 1829-1881, December.
    7. Luis Castro-Martín & Maria del Mar Rueda & Ramón Ferri-García, 2020. "Inference from Non-Probability Surveys with Statistical Matching and Propensity Score Adjustment Using Modern Prediction Techniques," Mathematics, MDPI, vol. 8(6), pages 1-19, June.
    8. 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.
    9. Xiaojun Mao & Zhonglei Wang & Shu Yang, 2023. "Matrix completion under complex survey sampling," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(3), pages 463-492, June.
    10. Ramón Ferri-García & Jean-François Beaumont & Keven Bosa & Joanne Charlebois & Kenneth Chu, 2022. "Weight smoothing for nonprobability surveys," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(3), pages 619-643, September.
    11. Daniele Cuntrera & Vincenzo Falco & Ornella Giambalvo, 2022. "On the Sampling Size for Inverse Sampling," Stats, MDPI, vol. 5(4), pages 1-15, November.
    12. Xinyu Li & Wang Miao & Fang Lu & Xiao‐Hua Zhou, 2023. "Improving efficiency of inference in clinical trials with external control data," Biometrics, The International Biometric Society, vol. 79(1), pages 394-403, March.
    13. Sixia Chen & Alexandra May Woodruff & Janis Campbell & Sara Vesely & Zheng Xu & Cuyler Snider, 2023. "Combining Probability and Nonprobability Samples by Using Multivariate Mass Imputation Approaches with Application to Biomedical Research," Stats, MDPI, vol. 6(2), pages 1-9, May.
    14. Maria del Mar Rueda, 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 1077-1081, December.
    15. Hengfang Wang & Jae Kwang Kim, 2025. "Information projection approach to smoothed propensity score weighting for handling selection bias under missing at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 77(1), pages 127-153, February.
    16. Ramón Ferri-García & María del Mar Rueda & Andrés Cabrera-León, 2021. "Self-Perceived Health, Life Satisfaction and Related Factors among Healthcare Professionals and the General Population: Analysis of an Online Survey, with Propensity Score Adjustment," Mathematics, MDPI, vol. 9(7), pages 1-27, April.
    17. Kamlesh Kumar Pandey & Diwakar Shukla, 2022. "Stratified linear systematic sampling based clustering approach for detection of financial risk group by mining of big data," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1239-1253, June.
    18. Ray Chambers & Setareh Ranjbar & Nicola Salvati & Barbara Pacini, 2022. "Weighting, informativeness and causal inference, with an application to rainfall enhancement," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1584-1612, October.
    19. Bing Li & Constantine Gatsonis & Issa J. Dahabreh & Jon A. Steingrimsson, 2023. "Estimating the area under the ROC curve when transporting a prediction model to a target population," Biometrics, The International Biometric Society, vol. 79(3), pages 2382-2393, September.
    20. Luis Castro-Martín & María del Mar Rueda & Ramón Ferri-García, 2020. "Estimating General Parameters from Non-Probability Surveys Using Propensity Score Adjustment," Mathematics, MDPI, vol. 8(11), pages 1-14, November.

    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:eee:matcom:v:227:y:2025:i:c:p:272-281. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.