RT info:eu-repo/semantics/article
T1 The joint role of trimming and constraints in robust estimation for mixtures of Gaussian factor analyzers.
A1 García Escudero, Luis Ángel
A1 Gordaliza Ramos, Alfonso
A1 Greselin, Francesca
A1 Ingrassia, Salvatore
A1 Mayo Iscar, Agustín
K1 Constrained estimation, Factor analyzers modeling, Mixture models, Model-based clustering, Robust estimation
AB Mixtures of Gaussian factors are powerful tools for modeling an unobserved heterogeneouspopulation, 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.
PB ELSEVIER
SN ISSN: 0167-9473
YR 2016
FD 2016
LK http://uvadoc.uva.es/handle/10324/21412
UL http://uvadoc.uva.es/handle/10324/21412
LA eng
NO Computational Statistics and Data Analysis, vol. 99, pp. 131-147.
NO Producción Científica
DS UVaDOC
RD 24-nov-2024