The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Flexible Imputation of Missing Data
Abstract
Missing data form a problem in every scientific discipline, yet the techniques required to handle them are complicated and often lacking. One of the great ideas in statistical science-multiple imputation-fills gaps in the data with plausible values, the uncertainty of which is coded in the data itself. It also solves other problems, many of which a
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“Item-level missing data in the completed surveys were imputed using the method of multiple imputation (MI) by chained equations (van Buuren, 2012).”
From WHO World Mental Health Surveys International College Student Project: Prevalence and distribution of mental disorders. (2018) · cites this paper as van Buuren, 2012
In the citing paper
“However, we imputed missing data using predictive mean matching and expectation maximization—two well-established imputation methods [42]-and sensitivity analyses based on both imputation techniques led to very similar results.”
In the citing paper
“All acquired data will be examined with respect to potential inconsistencies and missing values, which will be replaced using multiple imputation methods [42], if appropriate. To detect potential systematic biases, a drop-out analysis will be performed.”
From HELP@APP: Development and evaluation of a self-help app for traumatized Syrian refugees in Germany- A study protocol of a randomized controlled trial (2019) · cites this paper as [42]
In the citing paper
“In addition, multiple imputation (MI) by chained equations(Van Buuren, 2012) was used to adjust for within-survey item non-response, random internal subsampling of survey sections, and missing data due to skip logic errors that occurred in a few surveys.”
From Severe role impairment associated with mental disorders: Results of the WHO World Mental Health Surveys International College Student Project (2018) · cites this paper as Van Buuren, 2012
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Cited by
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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
Self-citationAuerbach, R. P., Collaborators, W. W.-I., Mortier, P., Bruffaerts, R., Alonso, J., Benjet, C., Cuijpers, P., Demyttenaere, K., Ebert, D. D., Green, J. G., et al. (2018) · Journal of Abnormal Psychology · doi:10.1037/abn0000362 · Cited by 2,522
missMDA : A Package for Handling Missing Values in Multivariate Data Analysis
Josse, J., & Husson, F. (2016) · Journal of Statistical Software · doi:10.18637/jss.v070.i01 · Cited by 1,423
Single-cell RNA-seq denoising using a deep count autoencoder
Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S., & Theis, F. J. (2019) · Nature Communications · doi:10.1038/s41467-018-07931-2 · Cited by 1,084
Missing Data in Clinical Research: A Tutorial on Multiple Imputation
Austin, P. C., White, I. R., Lee, D. S., & van Buuren, S. (2021) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2020.11.010 · Cited by 1,006
Metadata
- Authors
- van Buuren, S.
- Venue
- Chapman & Hall/CRC eBooks
- Publisher
- Chapman and Hall/CRC
- Published
- 2012-03-29
- OpenAlex
- W612972800
- Cited by
- 2,022
- References
- 0
- Citations by year
- 2026: 81 · 2025: 69 · 2024: 69 · 2023: 119 · 2022: 111 · 2021: 268 · 2020: 296 · 2019: 222 · 2018: 204 · 2017: 164 · 2016: 140 · 2015: 144 · 2014: 81 · 2013: 44 · 2012: 9
- FWCI
- 22.42
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Chapman & Hall/CRC eBooks (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
- Physical Sciences › Mathematics › Statistics and Probability › Data Analysis with R
- Keywords
- missing data, multiple imputation, uncertainty quantification, missing data imputation
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