Robust doubly protected estimators for quantiles with missing data

Abstract

Doubly protected methods are widely used for estimating the population mean of an outcome Y from a sample where the response is missing in some individuals. To compensate for the missing responses, a vector X of covariates is observed at each individual, and the missing mechanism is assumed to be independent of the response, conditioned on X (missing at random). In recent years, many authors have turned from the estimation of the mean to that of the median, and more generally, doubly protected estimators of the quantiles have been proposed. In this work, we present doubly protected estimators for the quantiles in semiparametric models that are also robust, in the sense that they are resistant to the presence of outliers in the sample