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Assessment of the effect of constraints in a new multivariate mixed method for statistical matching

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  • Claramunt González, Juan
  • van Delden, Arnout
  • de Waal, Ton

Abstract

A Multivariate Mixed method for Statistical Matching (MMSM) is proposed. The MMSM is a predictive mean matching method to impute values when integrating two datasets from the same population without overlapping units measuring several common and non-common variables. It considers the multivariate structure of the data by using multivariate Bayesian regression. The MMSM can also include auxiliary information from an additional dataset to improve the computation of intermediate values, and constraints to improve the selection of the donors. The results from a simulation study show that including information from an auxiliary dataset leads to far better results, especially in terms of bias and percentage of correct imputations. The inclusion of constraints also increases the quality of the imputations, and hence of the statistical matching.

Suggested Citation

  • Claramunt González, Juan & van Delden, Arnout & de Waal, Ton, 2023. "Assessment of the effect of constraints in a new multivariate mixed method for statistical matching," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    • Handle: RePEc:eee:csdana:v:177:y:2023:i:c:s0167947322001499
    DOI: 10.1016/j.csda.2022.107569
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    References listed on IDEAS

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    6. Pier Luigi Conti & Daniela Marella & Mauro Scanu, 2016. "Statistical Matching Analysis for Complex Survey Data With Applications," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1715-1725, October.
    7. Conti, Pier Luigi & Marella, Daniela & Scanu, Mauro, 2008. "Evaluation of matching noise for imputation techniques based on nonparametric local linear regression estimators," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 354-365, December.
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