Multiple imputation in quantile regression. Academic Article

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.

authors

• Carroll, Raymond

published proceedings

• Biometrika

author list (cited authors)

• Wei, Y., Ma, Y., & Carroll, R. J.

citation count

• 60

complete list of authors

• Wei, Ying||Ma, Yanyuan||Carroll, Raymond J

publication date

• June 2012

publisher

• Oxford University Press (OUP)  Publisher

published in

• Biometrika  Journal

keywords

• Missing Data
• Multiple Imputation
• Quantile Regression
• Regression Quantile
• Shrinkage Estimation

Digital Object Identifier (DOI)

• 10.1093/biomet/ass007

start page

• 423

end page

• 438

volume

• 99

issue

• 2

URL

• http%3A%2F%2Fdx.doi.org%2F10.1093%2Fbiomet%2Fass007