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

J.C. González, A. van Delden, T. de Waal*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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.
Original languageEnglish
Article number107569
Number of pages14
JournalComputational Statistics & Data Analysis
Volume177
DOIs
Publication statusPublished - 2023

Keywords

  • ADJUSTED WEIGHTS
  • Auxiliary dataset
  • FILE CONCATENATION
  • Hard constraints
  • IMPUTATION
  • Multiple imputation
  • Predictive mean matching
  • Soft constraints

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