2024-03-29T08:22:32Zhttp://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/214122021-06-23T10:09:46Zcom_10324_1151com_10324_931com_10324_894col_10324_1278
00925njm 22002777a 4500
dc
García Escudero, Luis Ángel
author
Gordaliza Ramos, Alfonso
author
Greselin, Francesca
author
Ingrassia, Salvatore
author
Mayo Iscar, Agustín
author
2016
Mixtures of Gaussian factors are powerful tools for modeling an unobserved heterogeneous
population, offering – at the same time – dimension reduction and model-based clustering. The high prevalence of spurious solutions and the disturbing effects of outlying observations in maximum likelihood estimation may cause biased or misleading inferences. Restrictions for the component covariances are considered in order to avoid spurious solutions, and trimming is also adopted, to provide robustness against violations of normality assumptions of the underlying latent factors. A detailed AECM algorithm for this new approach is presented. Simulation results and an application to the AIS dataset show the aim and effectiveness of the proposed methodology.
Computational Statistics and Data Analysis, vol. 99, pp. 131-147.
ISSN: 0167-9473
http://uvadoc.uva.es/handle/10324/21412
10.1016/j.csda.2016.01.005
131
147
Computational Statistics and Data Analysis,
99
Constrained estimation, Factor analyzers modeling, Mixture models, Model-based clustering, Robust estimation
The joint role of trimming and constraints in robust estimation for mixtures of Gaussian factor analyzers.