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dc.contributor.authorBlankmeyer, Eric ( )en_US
dc.date.accessioned2011-10-10T10:21:52Z
dc.date.available2012-02-24T10:21:52Z
dc.date.issued2011-10en_US
dc.identifier.citationBlankmeyer, E. (2011). Errors in variables or bad leverage at some observations? Texas State University-San Marcos, San Marcos, Texas.
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/4102
dc.description.abstractErrors-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 downweight or discard the problematic data. In simulations of simple and multiple regression where eiv affected 25% of the data and R2 was mediocre, one high-breakdown estimator had small bias, very good coverage, and precision that improved when the sample size increased.en_US
dc.formatText
dc.format.extent24 pages
dc.format.medium1 file (.pdf)
dc.language.isoen
dc.publisherTexas State University-San Marcos
dc.subjectErrors in variablesen_US
dc.subjectMeasurement erroren_US
dc.subjectHigh-breakdown estimatoren_US
dc.subjectMinimum covariance determinanten_US
dc.subjectOrthogonal regressionen_US
dc.subject.classificationEconometricsen_US
dc.titleErrors in Variables or Bad Leverage at Some Observations ?en_US
txstate.documenttypePaper
dc.description.departmentFinance and Economics


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