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ADDITIVE MODELS WITH PREDICTORS SUBJECT TO MEASUREMENT ERROR

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Summary

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.
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Keywords: Metropolis-Hastings; Monte Carlo expectation maximization; mixed models; nested EM; penalized splines; restricted maximum likelihood

Document Type: Research Article

Affiliations: Indian Institute of Management, University of Massachusetts and the University of New South Wales

Publication date: June 1, 2005

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