The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
MIDA: Multiple Imputation Using Denoising Autoencoders
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,265
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
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436
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
Schafer, J. L. (1999) · Statistical Methods in Medical Research · doi:10.1177/096228029900800102 · Cited by 3,949 · cited 1×
“This approach is called multiple imputation [10,15] and is the most widely used framework for missing data analytics.”
MethodStatistical Analysis with Missing Data
Bo., N., Little, R. J. A., & Rubin, D. B. (1988) · Population (French Edition) · doi:10.2307/1533221 · Cited by 2,095
The prevention and treatment of missing data in clinical trials
Little, R. J., D'Agostino, R., Cohen, M. L., Dickersin, K., Emerson, S. S., Farrar, J. T., Frangakis, C., Hogan, J. W., Molenberghs, G., Murphy, S. A., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmsr1203730 · Cited by 1,884
Missing-Data Adjustments in Large Surveys
Little, R. J. A. (1988) · Journal of Business & Economic Statistics · doi:10.1080/07350015.1988.10509663 · Cited by 1,101
Cited by
Machine learning and integrative analysis of biomedical big data
Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P. (2019) · Genes · doi:10.3390/genes10020087 · Cited by 380
Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks
Yoon, J., Zame, W. R., & van der Schaar, M. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2874712 · Cited by 341
Proceedings of the 19th Python in Science Conference
(2020) · Proceedings of the Python in Science Conference · doi:10.25080/majora-342d178e-02b · Cited by 284
Denoising temporal convolutional recurrent autoencoders for time series classification
Zheng, Z., Zhang, Z., Wang, L., & Luo, X. (2022) · Information Sciences · doi:10.1016/j.ins.2021.12.061 · Cited by 45
Bernardini, M., Doinychko, A., Romeo, L., Frontoni, E., & Amini, M.-R. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107188 · Cited by 42
Metadata
- Authors
- Gondara, L., & Wang, K.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 260-272
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2788592841
- Cited by
- 292
- References
- 16
- FWCI
- 56.61
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: MIDA: Multiple Imputation Using Denoising Autoencoders. https://app.cochat.ai/papers/10.1007/978-3-319-93040-4_21
- The paper
- Gondara, L., & Wang, K. (2018). MIDA: Multiple Imputation Using Denoising Autoencoders. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-93040-4_21
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
