The effect of errors in diagnosis and measurement on the estimation of the probability of an event

Joel E. Michalek, Ram C. Tripathi

Research output: Contribution to journalArticle

17 Scopus citations

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 languageEnglish (US)
Pages (from-to)713-721
Number of pages9
JournalJournal of the American Statistical Association
Volume75
Issue number371
DOIs
StatePublished - 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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