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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    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.
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