2018/07/08 by Eric Blankmeyer, Blankmeyer, Eric
Mathematics · Decision Sciences · #Advanced Statistical Methods and Models #Statistical Methods and Inference #Advanced Statistical Process Monitoring
paper · pdf · doi:10.48550/arxiv.1807.02814
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.