Imputation in Data Fusion of Heterogeneous Data Sets A Model-Based Numerical Experiment |
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Authors: | Andre Berchtold Andre Jeannin |
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Affiliation: | 1. Groupe de Recherche sur la Santé des Adolescents , University Hospital Center and University of Lausanne , Lausanne, Switzerland;2. Institut de Mathématiques Appliquées, University of Lausanne , Lausanne, Switzerland andre.berchtold@unil.ch;4. Groupe de Recherche sur la Santé des Adolescents , University Hospital Center and University of Lausanne , Lausanne, Switzerland |
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Abstract: | Given the very large amount of data obtained everyday through population surveys, much of the new research again could use this information instead of collecting new samples. Unfortunately, relevant data are often disseminated into different files obtained through different sampling designs. Data fusion is a set of methods used to combine information from different sources into a single dataset. In this article, we are interested in a specific problem: the fusion of two data files, one of which being quite small. We propose a model-based procedure combining a logistic regression with an Expectation-Maximization algorithm. Results show that despite the lack of data, this procedure can perform better than standard matching procedures. |
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Keywords: | Binary variable Data fusion Data structure Expectation-Maximization algorithm Logistic regression Matching |
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