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Sampling Techniques for Big Data Analysis

Author

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  • Jae Kwang Kim
  • Zhonglei Wang

Abstract

In analysing big data for finite population inference, it is critical to adjust for the selection bias in the big data. In this paper, we propose two methods of reducing the selection bias associated with the big data sample. The first method uses a version of inverse sampling by incorporating auxiliary information from external sources, and the second one borrows the idea of data integration by combining the big data sample with an independent probability sample. Two simulation studies show that the proposed methods are unbiased and have better coverage rates than their alternatives. In addition, the proposed methods are easy to implement in practice.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:istatr:v:87:y:2019:i:s1:p:s177-s191
    DOI: 10.1111/insr.12290
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    Cited by:

    1. Maciej Berȩsewicz & Dagmara Nikulin, 2021. "Estimation of the size of informal employment based on administrative records with non‐ignorable selection mechanism," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 667-690, June.
    2. 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.
    3. Carmen Sánchez-Cantalejo & María del Mar Rueda & Marc Saez & Iria Enrique & Ramón Ferri & Miguel de La Fuente & Román Villegas & Luis Castro & Maria Antònia Barceló & Antonio Daponte-Codina & Nicola L, 2021. "Impact of COVID-19 on the Health of the General and More Vulnerable Population and Its Determinants: Health Care and Social Survey–ESSOC, Study Protocol," IJERPH, MDPI, vol. 18(15), pages 1-20, July.
    4. Daniele Cuntrera & Vincenzo Falco & Ornella Giambalvo, 2022. "On the Sampling Size for Inverse Sampling," Stats, MDPI, vol. 5(4), pages 1-15, November.
    5. 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.
    6. 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.
    7. 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.
    8. 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.

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