TY - JOUR
T1 - Estimating a logistic discrimination functions when one of the training samples is subject to misclassification
T2 - A maximum likelihood approach
AU - Nagelkerke, Nico
AU - Fidler, Vaclav
N1 - Publisher Copyright:
© 2015 Nagelkerke, Fidler This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2015/10/16
Y1 - 2015/10/16
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pone.0140718
DO - 10.1371/journal.pone.0140718
M3 - Article
C2 - 26474313
AN - SCOPUS:84949036007
SN - 1932-6203
VL - 10
JO - PLoS ONE
JF - PLoS ONE
IS - 10
M1 - e0140718
ER -