Multiple imputation in quantile regression

Wei, Y; Ma, Y; Carroll, RJ

Multiple imputation in quantile regression

Wei, Y Ma, Y Carroll, RJ

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Publication Type:: Journal Article
Citation:: Biometrika, 2012, 99 (2), pp. 423 - 438
Issue Date:: 2012-06-01

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Field	Value	Language
dc.contributor.author	Wei, Y	en_US
dc.contributor.author	Ma, Y	en_US
dc.contributor.author	Carroll, RJ	en_US
dc.date.issued	2012-06-01	en_US
dc.identifier.citation	Biometrika, 2012, 99 (2), pp. 423 - 438	en_US
dc.identifier.issn	0006-3444	en_US
dc.identifier.uri	http://hdl.handle.net/10453/114931
dc.description.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 © 2012 Biometrika Trust.	en_US
dc.relation.ispartof	Biometrika	en_US
dc.relation.isbasedon	10.1093/biomet/ass007	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Multiple imputation in quantile regression	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	99	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	0103 Numerical and Computational Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	99	en_US

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 © 2012 Biometrika Trust.

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http://hdl.handle.net/10453/114931