This article investigates the effect of misclassification and measurement error in the basic data on the asymptotic bias and efficiency of the logistic regression (LR) and normal discrimination (ND) classification procedures. The effect of misclassification in a single binary independent variable on the bias and efficiency of both procedures is also presented. Typically, asymptotic bias increases and efficiency decreases as misclassification and measurement error increase. The performance of LR relative to ND is shown to be better in the presence of error than without error.
- Asymptotic relative efficiency
- Logistic regression
- Normal discrimination
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty