Survey Methodology
Survey data integration for regression analysis using model calibration

by Zhonglei Wang, Hang J. Kim and Jae Kwang KimNote 1

  • Release date: June 30, 2023
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Abstract

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.

Key Words: Big data; Empirical likelihood; Information projection; Measurement error models; Missing covariates.

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How to cite

Wang, Z., Kim, H.J. and Kim, J.K. (2023). Survey data integration for regression analysis using model calibration. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 49, No. 1. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2023001/article/00002-eng.htm.

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ISSN : 1492-0921

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Survey Methodology publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves. All papers will be refereed. However, the authors retain full responsibility for the contents of their papers and opinions expressed are not necessarily those of the Editorial Board or of Statistics Canada.

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Published by authority of the Minister responsible for Statistics Canada.

© His Majesty the King in Right of Canada, as represented by the Minister of Industry, 2023

Use of this publication is governed by the Statistics Canada Open Licence Agreement.

Catalogue No. 12-001-X

Frequency: Semi-annual

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