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Multiple imputation in quantile regression (2012)

Wei, Y., Ma, Y., Carroll, R. J.

Oxford University Press

In: Biometrika

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Publication Date: 2012-05-22

Description: 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.

Print ISSN: 0006-3444

Electronic ISSN: 1464-3510

Topics: Biology , Mathematics , Medicine

Published by Oxford University Press

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