<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Data fusion describes the method of combining data from (at least) two initially independent data sources to allow for joint analysis of variables which are not jointly observed. The fundamental idea is to base inference on identifying assumptions, and on common variables which provide information that is jointly observed in all the data sources. A popular class of methods dealing with this particular missing-data problem in practice is based on covariate-based nearest neighbour matching, whereas more flexible semi- or even fully parametric approaches seem underrepresented in applied data fusion. In this article we compare two different approaches of nearest neighbour hot deck matching: One, Random Hot Deck, is a variant of the covariate-based matching methods which was proposed by Eurostat, and can be considered as a ’classical’ statistical matching method, whereas the alternative approach is based on Predictive Mean Matching. We discuss results from a simulation study where we deviate from previous analyses of marginal distributions and consider joint distributions of fusion variables instead, and our findings suggest that Predictive Mean Matching tends to outperform Random Hot Deck.</p>
</abstract>

Data fusion describes the method of combining data from (at least) two initially independent data sources to allow for joint analysis of variables which are not jointly observed. The fundamental idea is to base inference on identifying assumptions, and on common variables which provide information that is jointly observed in all the data sources. A popular class of methods dealing with this particular missing-data problem in practice is based on covariate-based nearest neighbour matching, whereas more flexible semi- or even fully parametric approaches seem underrepresented in applied data fusion. In this article we compare two different approaches of nearest neighbour hot deck matching: One, Random Hot Deck, is a variant of the covariate-based matching methods which was proposed by Eurostat, and can be considered as a ’classical’ statistical matching method, whereas the alternative approach is based on Predictive Mean Matching. We discuss results from a simulation study where we deviate from previous analyses of marginal distributions and consider joint distributions of fusion variables instead, and our findings suggest that Predictive Mean Matching tends to outperform Random Hot Deck.

Data Fusion for Joining Income and Consumtion Information using Different Donor-Recipient Distance Metrics

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Data fusion describes the method of combining data from (at least) two initially independent data sources to allow for joint analysis of variables which are...

Data Fusion for Joining Income and Consumtion Information using Different Donor-Recipient Distance Metrics

Publié en ligne: 14 juin 2022

Pages: 509 - 532

Reçu: 01 nov. 2020

Accepté: 01 sept. 2021

DOI: https://doi.org/10.2478/jos-2022-0024

Mots clés
Statistical matching, missing data, predictive mean matching, nearest neighbour Imputation, missing-by-design pattern

© 2022 Florian Meinfelder et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Data Fusion for Joining Income and Consumtion Information using Different Donor-Recipient Distance Metrics

Publié en ligne: 14 juin 2022

Pages: 509 - 532

Reçu: 01 nov. 2020

Accepté: 01 sept. 2021

DOI: https://doi.org/10.2478/jos-2022-0024

Mots clésStatistical matching, missing data, predictive mean matching, nearest neighbour Imputation, missing-by-design pattern

© 2022 Florian Meinfelder et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Statistical matching, missing data, predictive mean matching, nearest neighbour Imputation, missing-by-design pattern