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  • 1. An Approximate Bayesian Approach to Improving Probability Sample Estimators Using a Supplementary Non-Probability Sample Archived
    Articles and reports: 11-522-X202100100008
    Description:

    Non-probability samples are being increasingly explored by National Statistical Offices as a complement to probability samples. We consider the scenario where the variable of interest and auxiliary variables are observed in both a probability and non-probability sample. Our objective is to use data from the non-probability sample to improve the efficiency of survey-weighted estimates obtained from the probability sample. Recently, Sakshaug, Wisniowski, Ruiz and Blom (2019) and Wisniowski, Sakshaug, Ruiz and Blom (2020) proposed a Bayesian approach to integrating data from both samples for the estimation of model parameters. In their approach, non-probability sample data are used to determine the prior distribution of model parameters, and the posterior distribution is obtained under the assumption that the probability sampling design is ignorable (or not informative). We extend this Bayesian approach to the prediction of finite population parameters under non-ignorable (or informative) sampling by conditioning on appropriate survey-weighted statistics. We illustrate the properties of our predictor through a simulation study.

    Key Words: Bayesian prediction; Gibbs sampling; Non-ignorable sampling; Statistical data integration.

    Release date: 2021-10-29
  • 2. Measuring the undercoverage of two data sources with a nearly perfect coverage through capture and recapture in the presence of linkage errors Archived
    Articles and reports: 11-522-X202100100006
    Description:

    In the context of its "admin-first" paradigm, Statistics Canada is prioritizing the use of non-survey sources to produce official statistics. This paradigm critically relies on non-survey sources that may have a nearly perfect coverage of some target populations, including administrative files or big data sources. Yet, this coverage must be measured, e.g., by applying the capture-recapture method, where they are compared to other sources with good coverage of the same populations, including a census. However, this is a challenging exercise in the presence of linkage errors, which arise inevitably when the linkage is based on quasi-identifiers, as is typically the case. To address the issue, a new methodology is described where the capture-recapture method is enhanced with a new error model that is based on the number of links adjacent to a given record. It is applied in an experiment with public census data.

    Key Words: dual system estimation, data matching, record linkage, quality, data integration, big data.

    Release date: 2021-10-22
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Articles and reports (2)

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

  • 1. An Approximate Bayesian Approach to Improving Probability Sample Estimators Using a Supplementary Non-Probability Sample Archived
    Articles and reports: 11-522-X202100100008
    Description:

    Non-probability samples are being increasingly explored by National Statistical Offices as a complement to probability samples. We consider the scenario where the variable of interest and auxiliary variables are observed in both a probability and non-probability sample. Our objective is to use data from the non-probability sample to improve the efficiency of survey-weighted estimates obtained from the probability sample. Recently, Sakshaug, Wisniowski, Ruiz and Blom (2019) and Wisniowski, Sakshaug, Ruiz and Blom (2020) proposed a Bayesian approach to integrating data from both samples for the estimation of model parameters. In their approach, non-probability sample data are used to determine the prior distribution of model parameters, and the posterior distribution is obtained under the assumption that the probability sampling design is ignorable (or not informative). We extend this Bayesian approach to the prediction of finite population parameters under non-ignorable (or informative) sampling by conditioning on appropriate survey-weighted statistics. We illustrate the properties of our predictor through a simulation study.

    Key Words: Bayesian prediction; Gibbs sampling; Non-ignorable sampling; Statistical data integration.

    Release date: 2021-10-29
  • 2. Measuring the undercoverage of two data sources with a nearly perfect coverage through capture and recapture in the presence of linkage errors Archived
    Articles and reports: 11-522-X202100100006
    Description:

    In the context of its "admin-first" paradigm, Statistics Canada is prioritizing the use of non-survey sources to produce official statistics. This paradigm critically relies on non-survey sources that may have a nearly perfect coverage of some target populations, including administrative files or big data sources. Yet, this coverage must be measured, e.g., by applying the capture-recapture method, where they are compared to other sources with good coverage of the same populations, including a census. However, this is a challenging exercise in the presence of linkage errors, which arise inevitably when the linkage is based on quasi-identifiers, as is typically the case. To address the issue, a new methodology is described where the capture-recapture method is enhanced with a new error model that is based on the number of links adjacent to a given record. It is applied in an experiment with public census data.

    Key Words: dual system estimation, data matching, record linkage, quality, data integration, big data.

    Release date: 2021-10-22
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