We present methods for diagnosing the effects of model misspecification of the true-predictor distribution in structural measurement error models. We first formulate latent-model robustness theoretically. Then we provide practical techniques for examining the adequacy of an assumed latent...

In clinical studies, covariates are often measured with error due to biological fluctuations, device error and other sources. Summary statistics and regression models that are based on mis-measured data will differ from the corresponding analysis based on the “true” covariate. Statistical...

Mixed effects models provide a suitable framework for statistical inference in a wide range of applications. The validity of likelihood inference for this class of models usually depends on the assumptions on random effects. We develop diagnostic tools for detecting random-effects model...

We derive a Monte-Carlo-amenable, minimum variance unbiased estimator of a nonlinear function of a normal mean and the variance of the estimator. Applications to problems arising in the analysis of data measured with error are described. Copyright 2005, Oxford University Press.