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Multiple Imputation Using Gaussian Copulas

Metternich, N; Hollenbach, FM; Bojinov, I; Minhas, S; Ward, MD; Volfovsky, A; (2019) Multiple Imputation Using Gaussian Copulas. Sociological Methods and Research 10.1177/0049124118799381. (In press). Green open access

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Abstract

Missing observations are pervasive throughout observational research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still rely on list-wise deletion. In this article, we present a simple to use approach to multiple imputation. We show that using Gaussian copulas for multiple imputation allows scholars to attain estimation results that have good coverage and small bias. Using simulated as well as observational data from published social science research we compare imputation via Gaussian copulas with two other widely used imputation methods: MICE and Amelia II. The three approaches perform relatively similarly. Importantly, however, imputation via the Gaussian copula is simple and does not require the researcher to undertake any transformation of the data or specification of distributional assumptions for individual variables but returns a valid posterior density of the imputed data.

Type: Article
Title: Multiple Imputation Using Gaussian Copulas
Open access status: An open access version is available from UCL Discovery
DOI: 10.1177/0049124118799381
Publisher version: https://doi.org/10.1177/0049124118799381
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Missing data, Bayesian statistics, categorical data
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Political Science
URI: https://discovery.ucl.ac.uk/id/eprint/10055548
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