Multiple imputation in quantile regression

• 成果类型: Article
• 署名作者: Wei, Ying; Ma, Yanyuan; Carroll, Raymond J.
• 署名单位: Columbia University; Texas A&M University System; Texas A&M University College Station
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/ass007
• 发表日期: 2012
• 页码: 423438
• 关键词: Longitudinal Data; estimators; models

摘要

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.