An Efficient Approach for Statistical Matching of Survey Data Trough Calibration, Optimal Transport and Balanced Sampling

TL;DR

This paper introduces an efficient statistical matching method that combines survey data using calibration, optimal transport, and balanced sampling to improve data integration and analysis.

Contribution

It presents a novel approach that effectively matches two samples with weighting schemes, enhancing data integration methods.

Findings

Improved matching accuracy over existing methods

Flexible variants for different data integration scenarios

Enhanced usability of combined survey datasets

Abstract

Statistical matching aims to integrate two statistical sources. These sources can be two samples or a sample and the entire population. If two samples have been selected from the same population and information has been collected on different variables of interest, then it is interesting to match the two surveys to analyse, for example, contingency tables or covariances. In this paper, we propose an efficient method for matching two samples that may each contain a weighting scheme. The method matches the records of the two sources. Several variants are proposed in order to create a directly usable file integrating data from both information sources.