Mixtures of Gaussian factors are powerful tools for modeling an unobserved heterogeneous population, offering at the same time dimensionreduction and model-based clustering. Unfortunately, the high prevalence of spurious solutions and the disturbing effects of outlying observations,along maximum likelihood estimation, open serious issues. We consider restrictions for the component covariances, to avoid spurious solutions, andtrimming, to provide robustness against violations of normality assumptions of the underlying latent factors. A detailed AECM algorithm for thisnew approach is presented. Simulation results and an application to the AIS dataset show the aim and effectiveness of the proposed methodology.
Robust estimation for mixtures of Gaussian factor analyzers
INGRASSIA, Salvatore;
2015-01-01
Abstract
Mixtures of Gaussian factors are powerful tools for modeling an unobserved heterogeneous population, offering at the same time dimensionreduction and model-based clustering. Unfortunately, the high prevalence of spurious solutions and the disturbing effects of outlying observations,along maximum likelihood estimation, open serious issues. We consider restrictions for the component covariances, to avoid spurious solutions, andtrimming, to provide robustness against violations of normality assumptions of the underlying latent factors. A detailed AECM algorithm for thisnew approach is presented. Simulation results and an application to the AIS dataset show the aim and effectiveness of the proposed methodology.File | Dimensione | Formato | |
---|---|---|---|
Garcia-Escudero_etal_ERCIM2015.pdf
solo gestori archivio
Licenza:
Non specificato
Dimensione
340.48 kB
Formato
Adobe PDF
|
340.48 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.