This thesis is devoted to the use of auxiliary information in sampling theory at the sampling stage and estimation stage. In Chapter 2, we give an overview of the key concepts of sampling theory. In Chapter 3, we propose an extension of the family of calibration estimators based on the use of complex parameters. In Chapter 4 and 5, we are interested in the simultaneous correction of sampling errors and nonresponse using a single calibration. It shows that despite the fact that the calibration does not explicitly use the response probabilities, it is necessary to write the response model to correctly select the calibration function. Otherwise, we run the risk of biased estimators whose bias can exceed the bias of the unadjusted estimator. In particular, in the case of generalized calibration, the variance and bias are amplified for calibration variables weakly correlated with the instrumental variables. In Chapter 6, we show that a conditional approach, based on the design, leads to estimators more robust to outliers and "jumpers strata. In Chapter 7, we highlight that the Fuller rejective sampling yield to a regression estimator which can be biased when the variable of interest does not follow a linear regression with the balancing variables.
