The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Multiple Imputation in Practice: Comparison of Software Packages for Regression Models With Missing Variables
Abstract
Missing data frequently complicates data analysis for scientific investigations. The development of statistical methods to address missing data has been an active area of research in recent decades. Multiple imputation, originally proposed by Rubin in a public use dataset setting, is a general purpose method for analyzing datasets with missing data that is broadly applicable to a variety of missing data settings. We review multiple imputation as an analytic strategy formissing data. Wedescribe and evaluate a number of software packages that implement this procedure, and contrast the interface, features, and results. We compare the packages, and detail shortcomings and useful features. The comparisons are illustrated using examples from an artificial dataset and a study of child psychopathology. We suggest additional features as well as discuss limitations and cautions to consider when using multiple imputation as an analytic strategy for incomplete data settings.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
Multiple imputation after 18+ years
Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476908 · Cited by 4,072
Schafer, J. L. (1999) · Statistical Methods in Medical Research · doi:10.1177/096228029900800102 · Cited by 3,949
Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation
King, G., Honaker, J., Joseph, A., & Scheve, K. (2001) · American Political Science Review · doi:10.1017/s0003055401000235 · Cited by 1,834
Cited by
mice: Multivariate Imputation by Chained Equations in R
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
Multiple imputation using chained equations: Issues and guidance for practice
White, I. R., Royston, P., & Wood, A. M. (2011) · Statistics in Medicine · doi:10.1002/sim.4067 · Cited by 9,792
Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M., & Carpenter, J. R. (2009) · BMJ · doi:10.1136/bmj.b2393 · Cited by 7,299
Multiple imputation of discrete and continuous data by fully conditional specification
van Buuren, S. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206074463 · Cited by 2,827
Metadata
- Authors
- Horton, N. J., & Lipsitz, S. R.
- Venue
- The American Statistician
- Publisher
- Informa UK Limited
- Published
- 2001-08
- Volume
- 55
- Issue
- 3
- Pages
- 244-254
- ISSN
- 0003-1305 (ISSN-L)
- 1537-2731
- OpenAlex
- W2076395872
- Cited by
- 600
- References
- 5
- FWCI
- 15.15
- Type
- journal article
- Language
- en
- Open access
- closed
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