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L. Régressions-gini-pls, nous introduisons des modèles de régression Gini-PLS " . Les algorithmes proposés combinent les propriétés des estimateurs relatifs aux régressions Gini et PLS Les quatre modèles construits dans cette thèse permettent de résoudre simultanément les problèmes : de valeurs extrêmes ( " outliers " ), de multi-colinéarité, de faible taille de l'échantillon, de données manquantes, d'erreurs de mesure et d'endogénéité. En présence des problèmes cités, les modèles uni-variés (Gini-PLS1) sont robustes pour estimer une variable dépendante en fonction d'une ou plusieurs variables explicatives, une application aux inégalités des revenus agricoles européens Résumé Dans cette thèse

M. Clés, Régressions Gini-PLS, problèmes économétriques (Multicolinéarité, endogénéité, erreurs de mesure, outliers, faible taille de l'échantillon et données manquantes, Approches RISD (Approches de régressions basées sur la décomposition en sources de revenus), Politique Agricole Commune