The DOI matches its Crossref record; 170 of the 170 references it cites were found in a registry or an index.
Design of inferential sensors in the process industry: A review of Bayesian methods
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Citations
Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
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
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Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
Model selection and multimodel inference: a practical information-theoretic approach.
Guthery, F. S., Burnham, K. P., & Anderson, D. R. (2003) · The Journal of Wildlife Management · doi:10.2307/3802723 · Cited by 41,495
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
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Multiple Imputation for Nonresponse in Surveys
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Statistical Analysis With Missing Data
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Cited by
Data Mining and Analytics in the Process Industry: The Role of Machine Learning
Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017) · IEEE Access · doi:10.1109/access.2017.2756872 · Cited by 922
Self-citationReview on data-driven modeling and monitoring for plant-wide industrial processes
Ge, Z. (2017) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2017.09.021 · Cited by 630
Zhu, J., Ge, Z., Song, Z., & Gao, F. (2018) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2018.09.003 · Cited by 308
Perera, Y. S., Ratnaweera, D. A. A. C., Dasanayaka, C. H., & Abeykoon, C. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105988 · Cited by 286
Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review
Brunner, V., Siegl, M., Geier, D., & Becker, T. (2021) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.722202 · Cited by 100
Metadata
- Authors
- Khatibisepehr, S., Huang, B., & Khare, S.
- Venue
- Journal of Process Control
- Publisher
- Elsevier BV
- Published
- 2013-11
- Volume
- 23
- Issue
- 10
- Pages
- 1575-1596
- ISSN
- 0959-1524 (ISSN-L)
- OpenAlex
- W2079692514
- Cited by
- 262
- References
- 170
- FWCI
- 18.85
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Khatibisepehr, S., Huang, B., & Khare, S. (2013). Design of inferential sensors in the process industry: A review of Bayesian methods. Journal of Process Control. https://doi.org/10.1016/j.jprocont.2013.05.007
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.
