title: Multiple imputation in quantile regression.
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-nconsistent 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.
authors.affiliation: Department of Biostatistics, Columbia University, 722 West 168th St., New York, New York 10032, U.S.A.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wei
authors.affiliation: Department of Statistics, Texas A&M University, College Station, Texas 77843-3143, U.S.A.
authors.forename: Yanyuan
authors.initials: Y
authors.lastname: Ma
authors.affiliation: Department of Statistics, Texas A&M University, College Station, Texas 77843-3143, U.S.A.
authors.forename: Raymond J
authors.initials: RJ
authors.lastname: Carroll
keywords: Missing data; Multiple imputation; Quantile regression; Regression quantile; Shrinkage estimation