Additive models with predictors subject to measurement error

Blackwell Publishing Ltd
Publication Type:
Journal Article
Australian & New Zealand Journal of Statistics, 2005, 47 (2), pp. 193 - 202
Issue Date:
Full metadata record
Files in This Item:
Filename Description SizeFormat
2010000113OK.pdf222.09 kBAdobe PDF
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.
Please use this identifier to cite or link to this item: