The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
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Citations
Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
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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McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991
The EM Algorithm and Extensions
Kushary, D. (1998) · Technometrics · doi:10.1080/00401706.1998.10485534 · Cited by 5,417
Proceedings of the fifth Berkeley symposium on mathematical statistics and probability.
Kojima, K.-I. (1969) · Europe PMC (PubMed Central) · Cited by 3,648
Modern Applied Statistics with S-Plus.
Venables, W. N., & Ripley, B. D. (1996) · Biometrics · doi:10.2307/2532871 · Cited by 2,994
Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability; Vol. IV
Roy, H. L. L., Lecam, L., & Neyman, J. (1969) · Revue de l'Institut International de Statistique / Review of the International Statistical Institute · doi:10.2307/1402306 · Cited by 2,835
A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms
Wei, G. C. G., & Tanner, M. A. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10474930 · Cited by 1,453
Mixture Models: Theory, Geometry and Applications
Lindsay, B. G. (1995) · NSF-CBMS Regional Conference Series in Probability and Statistics · doi:10.1214/cbms/1462106013 · Cited by 1,084
Cited by
An Approach for Determining the Number of Clusters in a Model-Based Cluster Analysis
Akogul, S., & Erisoglu, M. (2017) · Entropy · doi:10.3390/e19090452 · Cited by 269
Alvarez, M., Rahmani, E., Jew, B., Garske, K. M., Miao, Z., Benhammou, J. N., Ye, C. J., Pisegna, J. R., Pietiläinen, K. H., Halperin, E., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-67513-5 · Cited by 93
PlÖtz, T. (2022) · ACM Computing Surveys · doi:10.1145/3459666 · Cited by 34
Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets
Pettit, J.-B., Tomer, R., Achim, K., Richardson, S., Azizi, L., & Marioni, J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003824 · Cited by 23
Yang, L., McClean, S., Donnelly, M., Khan, K., & Burke, K. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3669942 · Cited by 9
Metadata
- Authors
- Biernacki, C., Celeux, G., & Govaert, G.
- Venue
- Computational Statistics & Data Analysis
- Publisher
- Elsevier BV
- Published
- 2003-01
- Volume
- 41
- Issue
- 3-4
- Pages
- 561-575
- ISSN
- 0167-9473 (ISSN-L)
- OpenAlex
- W1966129222
- Cited by
- 658
- References
- 18
- FWCI
- 11.31
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Biernacki, C., Celeux, G., & Govaert, G. (2003). Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models. Computational Statistics & Data Analysis. https://doi.org/10.1016/s0167-9473(02)00163-9
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.
