TY - JOUR AB - 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. 2012 Biometrika Trust2012 © 2012 Biometrika Trust. AU - Wei, Y AU - Ma, Y AU - Carroll, RJ DA - 2012/06/01 DO - 10.1093/biomet/ass007 EP - 438 JO - Biometrika PY - 2012/06/01 SP - 423 TI - Multiple imputation in quantile regression VL - 99 Y1 - 2012/06/01 Y2 - 2024/03/29 ER -