Add to ORKGInternational audienceWe propose a new methodology to select and rank covariates associated to a variable of interest in a context of high-dimensional data under dependence but few observations. The methodology imbricates successively rough selection, clustering of variables, decorrelation of variables using Factor Latent Analysis, selection using aggregation of adapted methods and finally ranking through bootstrap replications. Simulations study shows the interest of the decorrelation inside the different clusters of covariates. The methodology is applied to real dat
Clustering of variables is the task of grouping similar variables into different groups. It may be u...
This thesis concerns the analysis of high-dimensional and large-scale data that have become ubiq-uit...
International audienceHandling dependence or not in feature selection is still an open question in s...
International audienceWe propose a new methodology to select and rank covariates associated to a var...
International audienceThe analysis of high throughput data has renewed the statistical methodology f...
We propose a ranking-based variable selection (RBVS) technique that identifies important variables i...
International audienceWe propose a new methodology for selecting and ranking covariates associated w...
This paper is concerned with variable selection in linear high-dimensional framework when the set of...
Linear regression is a much applied technique in many research fields. Its aim is to predict one or...
Modern applications of statistical approaches involve high-dimensional complex data, where variable ...
International audienceStandard approaches to tackle high-dimensional supervised classification often...
We propose a new methodology to select and rank covariates associated to avariable of interest in...
• We estimate a sample covariance matrix Σ from empirical data. • Objective: infer dependence relati...
With the prevalence of high dimensional data, variable selection is crucial in many real application...
This research project has the objective to extend use of the matroid algorithm using statistically b...
Clustering of variables is the task of grouping similar variables into different groups. It may be u...
This thesis concerns the analysis of high-dimensional and large-scale data that have become ubiq-uit...
International audienceHandling dependence or not in feature selection is still an open question in s...
International audienceWe propose a new methodology to select and rank covariates associated to a var...
International audienceThe analysis of high throughput data has renewed the statistical methodology f...
We propose a ranking-based variable selection (RBVS) technique that identifies important variables i...
International audienceWe propose a new methodology for selecting and ranking covariates associated w...
This paper is concerned with variable selection in linear high-dimensional framework when the set of...
Linear regression is a much applied technique in many research fields. Its aim is to predict one or...
Modern applications of statistical approaches involve high-dimensional complex data, where variable ...
International audienceStandard approaches to tackle high-dimensional supervised classification often...
We propose a new methodology to select and rank covariates associated to avariable of interest in...
• We estimate a sample covariance matrix Σ from empirical data. • Objective: infer dependence relati...
With the prevalence of high dimensional data, variable selection is crucial in many real application...
This research project has the objective to extend use of the matroid algorithm using statistically b...
Clustering of variables is the task of grouping similar variables into different groups. It may be u...
This thesis concerns the analysis of high-dimensional and large-scale data that have become ubiq-uit...
International audienceHandling dependence or not in feature selection is still an open question in s...