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- W3022834251 abstract "• A procedure to select common variables based on their joint effect on Y and Z is proposed. • An algorithm to select common variables in presence of a high number of variables is proposed. • The algorithm relies on quantification of uncertainty in terms of partial identification regions and on a penalization terms taking into account the sparseness of contingency table obtained by the selection. Statistical matching aims at combining information available in distinct sample surveys referred to the same target population. The matching is usually based on a set of common variables shared by the available data sources. For matching purposes just a subset of all the common variables should be chosen, the so called matching variables. The paper presents a novel method for selecting the matching variables based on the analysis of the uncertainty characterizing the matching framework. The uncertainty is caused by unavailability of data for estimating parameters describing the association between variables not jointly observed in a single data source. The paper focuses on the case of categorical variables and presents a sequential procedure for identifying the most effective subset of common variables in reducing the overall uncertainty." @default.
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- W3022834251 date "2017-11-01" @default.
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- W3022834251 title "The use of uncertainty to choose matching variables in statistical matching" @default.
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- W3022834251 doi "https://doi.org/10.1016/j.ijar.2017.08.015" @default.
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