Measurement Errors as Bad Leverage Points

07/08/2018

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by   Eric Blankmeyer, et al.

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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 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 estimators have small bias and reliable confidence intervals.