Title: A methodology to select and rank covariates in high-dimensional data under dependence
Authors: Anne Gegout-Petit - INRIA Universite de Lorraine-IECL BIGS (France) [presenting]
Aurelie Muller-Guedin - University Nancy Lorraine (France)
Abstract: A methodology is proposed to select and rank covariates associated with a variable of interest in a context of high-dimensional data under dependence but few observations. The methodology imbricates successively clustering of covariates, decorrelation of covariates using Factor Latent Analysis, selection using aggregation of adapted methods and finally ranking. We present ``armada'', the package associated with the method. The performance of the method is assessed by simulations. An application to a real case in the framework of personalised medicine is given: the purpose is to find omics data linked with a biomarker of breast cancer.