Diagnostics for Multivariate Imputations

20 Pages Posted: 11 Sep 2007

See all articles by Andrew Gelman

Andrew Gelman

Columbia University - Department of Statistics and Department of Political Science

Marc A. Levy

Columbia University - Center for International Earth Science Information Network (CIESIN)

Kobi Abayomi

Columbia University

Date Written: June 14, 2007

Abstract

We consider three sorts of diagnostics for random imputations: (a) displays of the completed data, intended to reveal unusual patterns that might suggest problems with the imputations, (b) comparisons of the distributions of observed and imputed data values, and (c) checks of the fit of observed data to the model used to create the imputations. We formulate these methods in terms of sequential regression multivariate imputation [Van Buuren and Oudshoom 2000, and Raghunathan, Van Hoewyk, and Solenberger 2001], an iterative procedure in which the missing values of each variable are randomly imputed conditional on all the other variables in the completed data matrix. We also consider a recalibration procedure for sequential regression imputations. We apply these methods to the 2002 Environmental Sustainability Index (ESI), a linear aggregation of 64 environmental variables on 142 countries.

Keywords: imputations

Suggested Citation

Gelman, Andrew and Levy, Marc A. and Abayomi, Kobi, Diagnostics for Multivariate Imputations (June 14, 2007). Available at SSRN: https://ssrn.com/abstract=1010415 or http://dx.doi.org/10.2139/ssrn.1010415

Andrew Gelman (Contact Author)

Columbia University - Department of Statistics and Department of Political Science ( email )

New York, NY 10027
United States
212-854-4883 (Phone)
212-663-2454 (Fax)

Marc A. Levy

Columbia University - Center for International Earth Science Information Network (CIESIN) ( email )

New York, NY 10964
United States

Kobi Abayomi

Columbia University ( email )

3022 Broadway
New York, NY 10027
United States

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