Additive models with predictors subject to measurement error

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dc.contributor.author Ganguli, Bhaswati en_US
dc.contributor.author Staudenmayer, John en_US
dc.contributor.author Wand, Matthew en_US
dc.contributor.editor en_US
dc.date.accessioned 2011-02-07T06:17:48Z
dc.date.available 2011-02-07T06:17:48Z
dc.date.issued 2005 en_US
dc.identifier 2010000113 en_US
dc.identifier.citation Ganguli Bhaswati, Staudenmayer John, and Wand Matt 2005, 'Additive models with predictors subject to measurement error', Blackwell Publishing Ltd, vol. 47, no. 2, pp. 193-202. en_US
dc.identifier.issn 1369-1473 en_US
dc.identifier.other C1UNSUBMIT 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. en_US
dc.language en_US
dc.publisher Blackwell Publishing Ltd en_US
dc.relation.isbasedon http://dx.doi.org/10.1111/j.1467-842X.2005.00383.x en_US
dc.title Additive models with predictors subject to measurement error en_US
dc.parent Australian & New Zealand Journal of Statistics en_US
dc.journal.volume 47 en_US
dc.journal.number 2 en_US
dc.publocation Australia en_US
dc.identifier.startpage 193 en_US
dc.identifier.endpage 202 en_US
dc.cauo.name SCI.Mathematical Sciences en_US
dc.conference Verified OK en_US
dc.for 010400 en_US
dc.personcode 0000064878 en_US
dc.personcode 0000064879 en_US
dc.personcode 110509 en_US
dc.percentage 100 en_US
dc.classification.name Statistics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords * Metropolis-Hastings; * mixed models; * Monte Carlo expectation maximization; * nested EM; * penalized splines; * restricted maximum likelihood en_US
dc.staffid 110509 en_US


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