Additive models with predictors subject to measurement error

Ganguli, B; Staudenmayer, J; Wand, MP

Additive models with predictors subject to measurement error

Ganguli, B Staudenmayer, J Wand, MP

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Publication Type:: Journal Article
Citation:: Australian and New Zealand Journal of Statistics, 2005, 47 (2), pp. 193 - 202
Issue Date:: 2005-06-01

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Field	Value	Language
dc.contributor.author	Ganguli, B	en_US
dc.contributor.author	Staudenmayer, J	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2005-06-01	en_US
dc.identifier.citation	Australian and New Zealand Journal of Statistics, 2005, 47 (2), pp. 193 - 202	en_US
dc.identifier.issn	1369-1473	en_US
dc.identifier.uri	http://hdl.handle.net/10453/12986
dc.description.abstract	This paper develops a likelihood-based method for fitting additive models in the presence of measurement error. It formulates the additive model using the linear mixed model representation of penalized splines. In the presence of a structural measurement error model, the resulting likelihood involves intractable integrals, and a Monte Carlo expectation maximization strategy is developed for obtaining estimates. The method's performance is illustrated with a simulation study. © 2005 Australian Statistical Publishing Association Inc.	en_US
dc.relation.ispartof	Australian and New Zealand Journal of Statistics	en_US
dc.relation.isbasedon	10.1111/j.1467-842X.2005.00383.x	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Additive models with predictors subject to measurement error	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	47	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1403 Econometrics	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	47	en_US

Abstract:

This paper develops a likelihood-based method for fitting additive models in the presence of measurement error. It formulates the additive model using the linear mixed model representation of penalized splines. In the presence of a structural measurement error model, the resulting likelihood involves intractable integrals, and a Monte Carlo expectation maximization strategy is developed for obtaining estimates. The method's performance is illustrated with a simulation study. © 2005 Australian Statistical Publishing Association Inc.

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http://hdl.handle.net/10453/12986