Estimadores robustos doblemente protegidos.
Mariela Sued
FCEyN-UBA
Doubly protected estimators are widely used for estimating the population mean of an outcome Y from a sample with missing values, using a vector of covariates X. Many authors moved from the mean to the median, and more generally, doubly protected estimators of the quantiles have been proposed, assuming a parametric regression model for the relationship between X and Y and a parametric form for the propensity score. In this work, we present doubly protected estimators for the quantiles, that are also robust, in the sense that they are resistant to the presence of outliers in the sample. We also flexibilize the model for the relationship between X and Y. Thus we present robust doubly protected estimators for the quantiles of the response in the presence of missing observations, postulating a semiparametric regression model for the relationsip between the covariates and the response and a parametric model for the propensity score.