Journal
TEST
Volume 28, Issue 1, Pages 106-146Publisher
SPRINGER
DOI: 10.1007/s11749-018-0591-5
Keywords
Fisher consistency; Kernel weights; L-estimators; Marginal functionals; Missing at random; Semiparametric models
Categories
Funding
- ANPCYT [PICT 2014-0351]
- Universidad de Buenos Aires, Argentina [20020130100279BA]
- Ministry of Science and Innovation, Spain [MTM2013-41383P, MTM2016-76969P]
- Minerva Foundation
Ask authors/readers for more resources
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 -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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available