Measurement Errors as Bad Leverage Points
arXiv:1807.02814
Abstract
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.
20 pages, 1 figure
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