Multiple imputation in quantile regression

Ying Wei, Yanyuan Ma, Raymond J. Carroll

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

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. 2012 Biometrika Trust2012

Original languageEnglish (US)
Pages (from-to)423-438
Number of pages16
JournalBiometrika
Volume99
Issue number2
DOIs
StatePublished - Jun 2012

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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