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oai:uvadoc.uva.es:10324/214122021-06-23T10:09:46Zcom_10324_1151com_10324_931com_10324_894col_10324_1278
UVaDOC author García Escudero, Luis Ángel author Gordaliza Ramos, Alfonso author Greselin, Francesca author Ingrassia, Salvatore author Mayo Iscar, Agustín 2016-12-01T23:01:50Z 2016-12-01T23:01:50Z 2016 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 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. eng 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. info:eu-repo/semantics/article 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URL https://uvadoc.uva.es/bitstream/10324/21412/1/GGGIM_reviewedAlan.pdf File MD5 1578492618c13a01787d30663b1e28a1 943130 application/pdf GGGIM_reviewedAlan.pdf