Covariate Measurement Error in Logistic Regression.
Technical rept. Sep 84-Aug 85,
NORTH CAROLINA UNIV AT CHAPEL HILL DEPT OF STATISTICS
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In a logistic regression model when covariates are subject to measurement error the naive estimator, obtained by regressing on the observed covariates, is asymptotically biased. This document introduces a bias-adjusted estimator and two estimators appropriate for normally distributed measurement errors a functional maximum likelihood estimator and an estimator which exploits the consequences of sufficiency. The four proposals are studied asymptotically under conditions which are appropriate when the measurement error is small. A small Monte-Carlo study illustrates the superiority of the measurement-error estimators in certain situations. Additional keywords mathematical models.
- Statistics and Probability