Survey data integration for regression analysis using model calibration.
Survey data integration for regression analysis using model calibration.
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发表时间：
2021-07
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通讯作者：
Zhonglei Wang;Hang J Kim;Jae Kwang Kim
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作者：
Zhonglei Wang;Hang J Kim;Jae Kwang Kim
我们在数据集成的背景下考虑回归分析。为了结合来自外部来源的部分信息，我们采用模型校准的思想，它引入了基于观察到的协变量的“工作”简化模型。工作简化模型不一定是正确指定的，但可以是合并来自外部数据的部分信息的有用工具。实际实施基于经验似然法的新颖应用。所提出的方法对于组合来自具有不同缺失模式的多个来源的信息特别有吸引力。该方法应用于结合韩国国家健康和营养检查调查的调查数据和韩国国家健康保险共享服务的大数据的真实数据示例。
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 empirical likelihood method. 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.