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Statistical Matching Analysis for Complex Survey Data With Applications
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DOI：10.1080/01621459.2015.1112803.png)
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The goal of statistical matching is the estimation of a joint distribution having observed only samples from its marginals. The lack of joint observations on the variables of interest is the reason of uncertainty about the joint population distribution function. In the present article, the notion of matching error is introduced, and upper-bounded via an appropriate measure of uncertainty. Then, an estimate of the distribution function for the variables not jointly observed is constructed on the basis of a modification of the conditional independence assumption in the presence of logical constraints. The corresponding measure of uncertainty is estimated via sample data. Finally, a simulation study is performed, and an application to a real case is provided. Supplementary materials for this article are available online.
Keyword:
High entropy designs
Iterative proportional fitting (IPF) algorithm
Matching error
Uncertainty
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