Statistical Modelling 7 (2007), 255–273
Bayesian model selection for logistic regression with misclassified
outcomes
Richard Gerlach
Discipline of Econometrics and Business Statistics,
Faculty of Economics and Business,
University of Sydney, H04,
Sydney, NSW
Australia
eMail:
r.gerlach@econ.usyd.edu.au
James Stamey
Baylor University, Texas
USA
Abstract:
We consider the problem of variable selection for logistic regression
when the dependent variable is measured imperfectly, under both
differential and non-differential misclassification. An MCMC sampling
scheme is designed, incorporating uncertainty about which explanatory
variables affect the dependent variable and which affect the probability
of misclassification. We assume that a small gold standard perfectly
measured sample is available to augment the imperfectly measured sample,
under the differential misclassification framework. A simulation study
illustrates favourable results both in terms of variable selection and
parameter estimation. Examples analysing the risk of violence against
young women by their partner and the risk of injury in highway motor
accidents are considered.
Keywords:
logistic regression; Metropolis Hastings; misclassification;
model uncertainty
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