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<dc:title xml:lang="fr">Amélioration et développement de méthodes de sélection du nombre de composantes et de prédicteurs significatifs pour une régression PLS et certaines de ses extensions à l'aide du bootstrap</dc:title>
<dcterms:alternative xml:lang="en">lmprovement and development of selection methods for both the number of components and significant predictors for a PLS regression and some extensions with bootstrap techniques</dcterms:alternative>
<dc:subject xml:lang="fr">Régression PLS</dc:subject>
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<dc:subject xml:lang="fr">Puces à ADN</dc:subject>
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<dcterms:abstract xml:lang="fr">La régression Partial Least Squares (PLS), de part ses caractéristiques, est devenue une méthodologie statistique de choix pour le traitement de jeux de données issus d’études génomiques. La fiabilité de la régression PLS et de certaines de ses extensions repose, entre autres, sur une détermination robuste d’un hyperparamètre, le nombre de composantes. Une telle détermination reste un objectif important à ce jour, aucun critère existant ne pouvant être considéré comme globalement satisfaisant. Nous avons ainsi élaboré un nouveau critère de choix pour la sélection du nombre de composantes PLS basé sur la technique du bootstrap et caractérisé notamment par une forte stabilité. Nous avons ensuite pu l’adapter et l’utiliser à des fins de développement et d’amélioration de procédés de sélection de prédicteurs significatifs, ouvrant ainsi la voie à une identification rendue plus fiable et robuste des probe sets impliqués dans la caractéristique étudiée d’une pathologie.</dcterms:abstract>
<dcterms:abstract xml:lang="en">The Partial Least Squares (PLS) regression, through its properties, has become a versatile statistic methodology for the analysis of genomic datasets.The reliability of the PLS regression and some of its extensions relies on a robust determination of a tuning parameter, the number of components. Such a determination is still a major aim since no existing criterion could be considered as a global benchmark one in the state-of-art literature. We developed a new bootstrap based stopping criterion in PLS components construction that guarantee a high level of stability. We then adapted and used it to develop and improve variable selection processes, allowing a more reliable and robust determination of significant probe sets related to the studied feature of a pathology.</dcterms:abstract>
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