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Integrating probability and big non-probability samples data to produce Official Statistics

Author

Listed:
  • Natalia Golini

    (University of Turin)

  • Paolo Righi

    (Italian National Statistical Institute (Istat))

Abstract

This paper introduces the pseudo-calibration estimators, a novel method that integrates a non-probability sample of big size with a probability sample, assuming both samples contain relevant information for estimating the population parameter. The proposed estimators share a structural similarity with the adjusted projection estimators and the difference estimators but they adopt a different inferential approach and informative setup. The pseudo-calibration estimators can be employed when the target variable is observed in the probability sample and, in the non-probability sample, it is observed correctly, observed with error, or predicted. This paper also introduces an original application of the jackknife-type method for variance estimation. A simulation study shows that the proposed estimators are robust and efficient compared to the regression data integration estimators that use the same informative setup. Finally, a further evaluation using real data is carried out.

Suggested Citation

  • Natalia Golini & Paolo Righi, 2024. "Integrating probability and big non-probability samples data to produce Official Statistics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(2), pages 555-580, April.
    • Handle: RePEc:spr:stmapp:v:33:y:2024:i:2:d:10.1007_s10260-023-00740-y
    DOI: 10.1007/s10260-023-00740-y
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    References listed on IDEAS

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    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. Jelke Bethlehem, 2010. "Selection Bias in Web Surveys," International Statistical Review, International Statistical Institute, vol. 78(2), pages 161-188, August.
    3. repec:bla:istatr:v:83:y:2015:i:3:p:436-448 is not listed on IDEAS
    4. 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.
    5. Jae‐Kwang Kim & Siu‐Ming Tam, 2021. "Data Integration by Combining Big Data and Survey Sample Data for Finite Population Inference," International Statistical Review, International Statistical Institute, vol. 89(2), pages 382-401, August.
    Full references (including those not matched with items on IDEAS)

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