Abstract
In this thesis we propose ”Gini-PLS” regressions. The proposed algorithms combine the properties of the estimators related to the Gini and PLS regressions. The four models built in this thesis solve simultaneously the problems of : extreme values (outliers), multicollinearity, small sample, missing data, measurement errors,and endogeneity. In presence of these problems, the univariate models (Gini-PLS1) are robust to estimate a dependent variable with one or more explanatory variables. While, the multivariate models (Gini-PLS2) are used to estimate a matrix of dependent variables with a matrix of explanatory variables.Our application in this thesis is the estimation of the contributions of technico-economic variables to the whole inequality of farm’s income for European countries acceding to the Common Agricultural Policy. We also propose Gini-PLS regressions approaches based on income source decomposition (RISD-Gini-PLS) to estimate the contributions of techno-economic variables (income sources, areas, labor, etc.) to the incomei nequalies of productions (total output crops and output livestock) for european countries.