Estimation and inference of error-prone covariate effect in the presence of confounding variables

Liu, J; Ma, Y; Zhu, L; Carroll, RJ

Estimation and inference of error-prone covariate effect in the presence of confounding variables

Liu, J Ma, Y Zhu, L Carroll, RJ

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Publication Type:: Journal Article
Citation:: Electronic Journal of Statistics, 2017, 11 (1), pp. 480 - 501
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Liu, J	en_US
dc.contributor.author	Ma, Y	en_US
dc.contributor.author	Zhu, L	en_US
dc.contributor.author	Carroll, RJ https://orcid.org/0000-0002-5465-9682	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Electronic Journal of Statistics, 2017, 11 (1), pp. 480 - 501	en_US
dc.identifier.issn	1935-7524	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127046
dc.description.abstract	© 2017, Institute of Mathematical Statistics. All rights reserved. We introduce a general single index semiparametric measurement error model for the case that the main covariate of interest is measured with error and modeled parametrically, and where there are many other variables also important to the modeling. We propose a semiparametric bias-correction approach to estimate the effect of the covariate of interest. The resultant estimators are shown to be root-n consistent, asymptotically normal and locally efficient. Comprehensive simulations and an analysis of an empirical data set are performed to demonstrate the finite sample performance and the bias reduction of the locally efficient estimators.	en_US
dc.relation.ispartof	Electronic Journal of Statistics	en_US
dc.relation.isbasedon	10.1214/17-EJS1242	en_US
dc.title	Estimation and inference of error-prone covariate effect in the presence of confounding variables	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	11	en_US
utslib.for	0104 Statistics	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	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	11	en_US

Abstract:

© 2017, Institute of Mathematical Statistics. All rights reserved. We introduce a general single index semiparametric measurement error model for the case that the main covariate of interest is measured with error and modeled parametrically, and where there are many other variables also important to the modeling. We propose a semiparametric bias-correction approach to estimate the effect of the covariate of interest. The resultant estimators are shown to be root-n consistent, asymptotically normal and locally efficient. Comprehensive simulations and an analysis of an empirical data set are performed to demonstrate the finite sample performance and the bias reduction of the locally efficient estimators.

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http://hdl.handle.net/10453/127046