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

Measurement Errors as Bad Leverage Points