Plug-in marginal estimation under a general regression model with missing responses and covariates

BIANCO, ANA; BOENTE, GRACIELA; GONZALEZ-MANTEIGA, WENCESLAO; PEREZ-GONZALEZ, ANA

TEST

SPRINGER

Lugar: Berlin; Año: 2019 vol. 108 p. 106 - 146

1133-0686

In this paper, we consider a general regression model where missing data occur in the response and in the covariates. Our aim is to estimate the marginal distribution function and a marginal functional, such as the mean, the median or any $alpha-$quantile of the response variable. A missing at random condition is assumed in order to prevent from bias in the estimation of the marginal measures under a non--ignorable missing mechanism. We give two different approaches for the estimation of the responses distribution function and of a given marginal functional, involving inverse probability weighting and the convolution of the distribution function of the observed residuals and that of the observed estimated regression function. Through a Monte Carlo study and two real data sets we illustrate the behaviour of our proposals.