  Errors-in-variables is a long-standing, difficult issue in linear regression;
and progress depends in part on new identifying assumptions. I characterize
measurement error as bad-leverage points and assume that fewer than half the
sample observations are heavily contaminated, in which case a high-breakdown
robust estimator may be able to isolate and down weight or discard the
problematic data. In simulations of simple and multiple regression where eiv
affects 25% of the data and R-squared is mediocre, certain high-breakdown
estimators have small bias and reliable confidence intervals.
