Additive models with predictors subject to measurement error

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Journal Article
Australian and New Zealand Journal of Statistics, 2005, 47 (2), pp. 193 - 202
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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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