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  • 1. Correcting Selection Bias in a Non-probability Two-phase Payment Survey Archived
    Articles and reports: 11-522-X202500100007
    Description: This paper employs the Pseudo Maximum Likelihood (PML) estimator to the non-probability two-phase sampling when relevant auxiliary information is available from both probability survey sample and non-probability survey sample. To accommodate various weight adjustments and estimates variance beyond totals and means such as medians and quantiles, a simplified pseudo-population bootstrap procedure is proposed to approximately estimate the second-phase variance. Specifically, the simplification ignores the second phase sampling variability (i.e., treated as fixed, while in fact it is random), if the first-phase sampling fraction of the non-probability sample is negligible. Using the Bank of Canada 2020 Cash Alternative Survey Wave 2, the performance of the proposed method is compared to alternative methods, which either do not explicitly model the selection probability (i.e., raking) or ignore the valuable information from Phase 1 (i.e., Phase-2-Only). The results show that the PML-based approach performs better than raking and Phase-2-Only estimates in terms of reducing the selection bias for both phases' payment-related variables, especially for the low-response youth group. Estimated variances of the PML-based estimates are stable.

    Release date: 2025-09-08
  • 2. On the use of machine learning methods for the treatment of unit nonresponse in surveys
    Articles and reports: 12-001-X202500100003
    Description: In recent years, there has been a significant interest in machine learning in national statistical offices. Thanks to their flexibility, these methods may prove useful at the nonresponse treatment stage. In this article, we conduct an empirical investigation in order to compare several machine learning procedures in terms of bias and efficiency. In addition to the classical machine learning procedures, we assess the performance of ensemble approaches that make use of different machine learning procedures to produce a set of weights adjusted for nonresponse.
    Release date: 2025-06-30
Articles and reports (2)

Articles and reports (2) ((2 results))

  • 1. Correcting Selection Bias in a Non-probability Two-phase Payment Survey Archived
    Articles and reports: 11-522-X202500100007
    Description: This paper employs the Pseudo Maximum Likelihood (PML) estimator to the non-probability two-phase sampling when relevant auxiliary information is available from both probability survey sample and non-probability survey sample. To accommodate various weight adjustments and estimates variance beyond totals and means such as medians and quantiles, a simplified pseudo-population bootstrap procedure is proposed to approximately estimate the second-phase variance. Specifically, the simplification ignores the second phase sampling variability (i.e., treated as fixed, while in fact it is random), if the first-phase sampling fraction of the non-probability sample is negligible. Using the Bank of Canada 2020 Cash Alternative Survey Wave 2, the performance of the proposed method is compared to alternative methods, which either do not explicitly model the selection probability (i.e., raking) or ignore the valuable information from Phase 1 (i.e., Phase-2-Only). The results show that the PML-based approach performs better than raking and Phase-2-Only estimates in terms of reducing the selection bias for both phases' payment-related variables, especially for the low-response youth group. Estimated variances of the PML-based estimates are stable.

    Release date: 2025-09-08
  • 2. On the use of machine learning methods for the treatment of unit nonresponse in surveys
    Articles and reports: 12-001-X202500100003
    Description: In recent years, there has been a significant interest in machine learning in national statistical offices. Thanks to their flexibility, these methods may prove useful at the nonresponse treatment stage. In this article, we conduct an empirical investigation in order to compare several machine learning procedures in terms of bias and efficiency. In addition to the classical machine learning procedures, we assess the performance of ensemble approaches that make use of different machine learning procedures to produce a set of weights adjusted for nonresponse.
    Release date: 2025-06-30

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