ADDITIVE MODELS WITH PREDICTORS SUBJECT TO MEASUREMENT ERROR |
| |
| Authors: | Bhaswati Ganguli John Staudenmayer M.P. Wand |
| |
| Affiliation: | Indian Institute of Management, University of Massachusetts and the University of New South Wales |
| |
| 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. |
| |
| Keywords: | Metropolis-Hastings mixed models Monte Carlo expectation maximization nested EM penalized splines restricted maximum likelihood |
|
