Estimation efficace en présence de non-réponse dans les enquêtes
The efficient use of auxiliary information for estimating descriptive parameters in a finite population in the presence of unit nonresponse has become quite common. This thesis deals with reweighting procedures that involve a change in respondent weight to address unit non-response. A known approach is the propensity score adjustment, which consists of multiplying the base weight by the inverse of the estimated response probability obtained using a response model. In order to improve the efficiency of the propensity score adjusted estimators, we propose a method based on model calibration. This method involves modeling the relationship between survey variables identified as key variables and a vector of auxiliary variables on incorporating the auxiliary information into predicted values obtained by the model, which leads to a single weighting system. The resulting estimator can improve efficiency by making more use of the available auxiliary information. A simulation study evaluates the properties of the estimators in terms of bias and mean square error.
Auxiliary information Calibration Model calibration Non-response Propensity score adjusted estimator Re-weighting