Imputation in data fusion of heterogeneous data sets a model-based numerical experiment

Details

Serval ID
serval:BIB_42AD4B036A5C
Type
Article: article from journal or magazin.
Collection
Publications
Title
Imputation in data fusion of heterogeneous data sets a model-based numerical experiment
Journal
Communications In Statistics-Simulation and Computation
Author(s)
Berchtold Andre, Jeannin Andre
ISSN
0361-0918
Publication state
Published
Issued date
2008
Peer-reviewed
Oui
Volume
37
Number
7
Pages
1316-1328
Language
english
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.
Keywords
binary variable, data fusion, data structure, Expectation-Maximization algorithm, logistic regression, matching, MULTIPLE IMPUTATION
Web of science
Create date
29/09/2009 16:51
Last modification date
20/08/2019 14:45
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