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  • 1. Survey data integration for regression analysis using model calibration
    Articles and reports: 12-001-X202300100002
    Description: We consider regression analysis in the context of data integration. To combine partial information from external sources, we employ the idea of model calibration which introduces a “working” reduced model based on the observed covariates. The working reduced model is not necessarily correctly specified but can be a useful device to incorporate the partial information from the external data. The actual implementation is based on a novel application of the information projection and model calibration weighting. The proposed method is particularly attractive for combining information from several sources with different missing patterns. The proposed method is applied to a real data example combining survey data from Korean National Health and Nutrition Examination Survey and big data from National Health Insurance Sharing Service in Korea.
    Release date: 2023-06-30
  • 2. Comments by Zhonglei Wang and Jae Kwang Kim on “Statistical inference with non-probability survey samples”
    Articles and reports: 12-001-X202200200007
    Description:

    Statistical inference with non-probability survey samples is a notoriously challenging problem in statistics. We introduce two new methods of nonparametric propensity score technique for weighting in the non-probability samples. One is the information projection approach and the other is the uniform calibration in the reproducing kernel Hilbert space.

    Release date: 2022-12-15
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Articles and reports (2)

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

  • 1. Survey data integration for regression analysis using model calibration
    Articles and reports: 12-001-X202300100002
    Description: We consider regression analysis in the context of data integration. To combine partial information from external sources, we employ the idea of model calibration which introduces a “working” reduced model based on the observed covariates. The working reduced model is not necessarily correctly specified but can be a useful device to incorporate the partial information from the external data. The actual implementation is based on a novel application of the information projection and model calibration weighting. The proposed method is particularly attractive for combining information from several sources with different missing patterns. The proposed method is applied to a real data example combining survey data from Korean National Health and Nutrition Examination Survey and big data from National Health Insurance Sharing Service in Korea.
    Release date: 2023-06-30
  • 2. Comments by Zhonglei Wang and Jae Kwang Kim on “Statistical inference with non-probability survey samples”
    Articles and reports: 12-001-X202200200007
    Description:

    Statistical inference with non-probability survey samples is a notoriously challenging problem in statistics. We introduce two new methods of nonparametric propensity score technique for weighting in the non-probability samples. One is the information projection approach and the other is the uniform calibration in the reproducing kernel Hilbert space.

    Release date: 2022-12-15
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