Multiple imputation in quantile regression #MMPMID24944347
Wei Y; Ma Y; Carroll RJ
Biometrika 2012[Jun]; 99 (2): 423-38 PMID24944347show ga
We propose a multiple imputation estimator for parameter estimation in a quantile regression model when some covariates are missing at random. The estimation procedure fully utilizes the entire dataset to achieve increased efficiency, and the resulting coefficient estimators are root-n consistent and asymptotically normal. To protect against possible model misspecification, we further propose a shrinkage estimator, which automatically adjusts for possible bias. The finite sample performance of our estimator is investigated in a simulation study. Finally, we apply our methodology to part of the Eating at American?s Table Study data, investigating the association between two measures of dietary intake.