Estimating a logistic discrimination functions when one of the training samples is subject to misclassification: A maximum likelihood approach

Nico Nagelkerke, Vaclav Fidler

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The problem of discrimination and classification is central to much of epidemiology. Here we consider the estimation of a logistic regression/discrimination function from training samples, when one of the training samples is subject to misclassification or mislabeling, e.g. diseased individuals are incorrectly classified/labeled as healthy controls. We show that this leads to zero-inflated binomial model with a defective logistic regression or discrimination function, whose parameters can be estimated using standard statistical methods such as maximum likelihood. These parameters can be used to estimate the probability of true group membership among those, possibly erroneously, classified as controls. Two examples are analyzed and discussed. A simulation study explores properties of the maximum likelihood parameter estimates and the estimates of the number of mislabeled observations.

Original languageEnglish
Article numbere0140718
JournalPLoS ONE
Volume10
Issue number10
DOIs
Publication statusPublished - Oct 16 2015
Externally publishedYes

ASJC Scopus subject areas

  • General

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