Parametric estimation is complicated when data are measured with error. The problem of regression modeling when one or more covariates are measured with error is considered in this paper. It is often the case that, evaluated at the observed error-prone data, the unbiased true-data estimating...

Confidence intervals are one of the most useful statistical tools. This dissertation is a study of several methods for forming confidence intervals that are insensitive to model assumptions, provided that the mean model for the data is not misspecified. The most commonly used robust confidence...