A mixed model approach to measurement error in semiparametric regression

An essential assumption in traditional regression techniques is that predictors are measured without errors. Failing to take into account measurement error in predictors may result in severely biased inferences. Correcting measurement-error bias is an extremely difficult problem when estimating a regression function nonparametrically. We propose an approach to deal with measurement errors in predictors when modelling flexible regression functions. This approach depends on directly modelling the mean and the variance of the response variable after integrating out the true unobserved predictors in a penalized splines model. We demonstrate through simulation studies that our approach provides satisfactory prediction accuracy largely outperforming previously suggested local polynomial estimators even when the model is incorrectly specified and is competitive with the Bayesian estimator.

Automated summary

The paper proposes an approach to handle measurement errors in predictors when modeling flexible regression functions by directly modeling the mean and variance of the response variable after integrating out the true unobserved predictors in a penalized splines model. Simulation studies show that this approach provides satisfactory prediction accuracy, outperforming local polynomial estimators and being competitive with the Bayesian estimator even when the model is incorrectly specified.