A simulation study to compare robust clustering methods based on mixtures
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Bibliographic InfoArticle provided by Springer in its journal Advances in Data Analysis and Classification.
Volume (Year): 4 (2010)
Issue (Month): 2 (September)
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Web page: http://www.springer.com/statistics/statistical+theory+and+methods/journal/11634
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- Karlis, Dimitris & Xekalaki, Evdokia, 2003. "Choosing initial values for the EM algorithm for finite mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 577-590, January.
- Neykov, N. & Filzmoser, P. & Dimova, R. & Neytchev, P., 2007. "Robust fitting of mixtures using the trimmed likelihood estimator," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 299-308, September.
- L. García-Escudero & A. Gordaliza & A. Mayo-Iscar, 2014. "A constrained robust proposal for mixture modeling avoiding spurious solutions," Advances in Data Analysis and Classification, Springer, vol. 8(1), pages 27-43, March.
- Christian Hennig, 2013. "Discussion of “Model-based clustering with non-normal mixture distributions” by S. X. Lee and G. J. McLachlan," Statistical Methods and Applications, Springer, vol. 22(4), pages 455-458, November.
- Luca De Angelis, 2013. "Latent class models for financial data analysis: some statistical developments," Statistical Methods and Applications, Springer, vol. 22(2), pages 227-242, June.
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