The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Missing data imputation using statistical and machine learning methods in a real breast cancer problem
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Citations
Cites
Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793
Regression Models and Life-Tables
Cox, D. R. (1992) · Springer Series in Statistics · doi:10.1007/978-1-4612-4380-9_37 · Cited by 26,297
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
(2012) · Encyclopedia of the Sciences of Learning · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,370
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
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
(2006) · Springer eBooks · Cited by 8,123
Russell, E. L., Chiang, L. H., & Braatz, R. D. (2000) · Advances in Industrial Control · doi:10.1007/978-1-4471-0409-4_3 · Cited by 7,862
The use of ranks to avoid the assumption of normality implicit in the analysis of variance
Friedman, M. (1937) · Journal of the American Statistical Association · doi:10.1080/01621459.1937.10503522 · Cited by 7,005
Cited by
A survey on missing data in machine learning
Emmanuel, T., Maupong, T., Mpoeleng, D., Semong, T., Mphago, B., & Tabona, O. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00516-9 · Cited by 1,067
A Review on Soft Sensors for Monitoring, Control, and Optimization of Industrial Processes
Jiang, Y., Yin, S., Dong, J., & Kaynak, O. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3033153 · Cited by 667
Missing value imputation: a review and analysis of the literature (2006–2017)
Lin, W.-C., & Tsai, C.-F. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09709-4 · Cited by 612
Machine learning in medical applications: a review of state-of-the-art methods
Shehab, M., Abualigah, L., Shambour, Q., Abu-Hashem, M. A., Shambour, M. K. Y., Alsalibi, A. I., & Gandomi, A. H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105458 · Cited by 542
Machine learning, statistical learning and the future of biological research in psychiatry
Iniesta, R., Stahl, D., & McGuffin, P. (2016) · Psychological Medicine · doi:10.1017/s0033291716001367 · Cited by 328
Metadata
- Authors
- Jerez, J. M., Molina, I., García-Laencina, P. J., Alba, E., Ribelles, N., Martín, M., & Franco, L.
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2010-10
- Volume
- 50
- Issue
- 2
- Pages
- 105-115
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W1977185509
- Cited by
- 607
- References
- 50
- FWCI
- 19.54
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Missing data imputation using statistical and machine learning methods in a real breast cancer problem. https://app.cochat.ai/papers/10.1016/j.artmed.2010.05.002
- The paper
- Jerez, J. M., Molina, I., García-Laencina, P. J., Alba, E., Ribelles, N., Martín, M., & Franco, L. (2010). Missing data imputation using statistical and machine learning methods in a real breast cancer problem. Artificial Intelligence in Medicine. https://doi.org/10.1016/j.artmed.2010.05.002
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.
