Concentration graph models are an interesting framework to explore the conditional independence structure in a multivariate Normal distribution. The SINFUL procedure (Drton, 2004) is an appealing method for model selection in these kinds of models. In this paper, we propose a simple way to robustify SINFUL procedure to the presence of outliers. The solution proposed is based on the minimum covariance determinant estimator for the variance-covariance matrix. A simulation study confirmed that our choice is a useful way to robustify the estimates and the model selection procedure in concentration graph models.
Robustifying SINful procedure / Gottard, Anna; Pacillo, S.. - STAMPA. - (2007), pp. 577-580. (Intervento presentato al convegno CLADAG 2007 tenutosi a Università di Macerata).
Robustifying SINful procedure
GOTTARD, ANNA;
2007
Abstract
Concentration graph models are an interesting framework to explore the conditional independence structure in a multivariate Normal distribution. The SINFUL procedure (Drton, 2004) is an appealing method for model selection in these kinds of models. In this paper, we propose a simple way to robustify SINFUL procedure to the presence of outliers. The solution proposed is based on the minimum covariance determinant estimator for the variance-covariance matrix. A simulation study confirmed that our choice is a useful way to robustify the estimates and the model selection procedure in concentration graph models.File | Dimensione | Formato | |
---|---|---|---|
cladag07.pdf
Accesso chiuso
Tipologia:
Pdf editoriale (Version of record)
Licenza:
Tutti i diritti riservati
Dimensione
184.92 kB
Formato
Adobe PDF
|
184.92 kB | Adobe PDF | Richiedi una copia |
I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.