Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 713-721 |
| Number of pages | 9 |
| Journal | Journal of the American Statistical Association |
| Volume | 75 |
| Issue number | 371 |
| DOIs | |
| State | Published - Sep 1980 |
Keywords
- Asymptotic relative efficiency
- Logistic regression
- Normal discrimination
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
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Michalek, J. E., & Tripathi, R. C. (1980). The effect of errors in diagnosis and measurement on the estimation of the probability of an event. Journal of the American Statistical Association, 75(371), 713-721. https://doi.org/10.1080/01621459.1980.10477539