  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.
