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Multiple data sources are becoming increasingly available for statisticalanalyses in the era of big data. As an important example in finite-populationinference, we consider an imputation approach to combining a probability samplewith big observational data. Unlike the usual imputation for missing dataanalysis, we create imputed values for the whole elements in the probabilitysample. Such mass imputation is attractive in the context of survey dataintegration (Kim and Rao, 2012). We extend mass imputation as a tool for dataintegration of survey data and big non-survey data. The mass imputation methodsand their statistical properties are presented. The matching estimator ofRivers (2007) is also covered as a special case. Variance estimation withmass-imputed data is discussed. The simulation results demonstrate the proposedestimators outperform existing competitors in terms of robustness andefficiency.

Integration of survey data and big observational data for finite population inference using mass imputation