Multiple imputation in quantile regression. Academic Article uri icon

abstract

  • 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.

author list (cited authors)

  • Wei, Y., Ma, Y., & Carroll, R. J.

citation count

  • 49

publication date

  • March 2012