Binomial regression with misclassification.
Motivated by a study of human papillomavirus infection in women, we present a Bayesian binomial regression analysis in which the response is subject to an unconstrained misclassification process. Our iterative approach provides inferences for the parameters that describe the relationships of the covariates with the response and for the misclassification probabilities. Furthermore, our approach applies to any meaningful generalized linear model, making model selection possible. Finally, it is straightforward to extend it to multinomial settings.
Instituto Superior Técnico, e Centro de Matemática e Aplicações, Universidade Técnica de Lisboa, Portugal. dpaulino@math.ist.utl.pt
Carlos Daniel
CD
Paulino
Paulo
P
Soares
John
J
Neuhaus
D001499:Bayes Theorem; D001699:Biometry; D005188:False Negative Reactions; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D027383:Papillomaviridae; D030361:Papillomavirus Infections / Q000175:diagnosis; D012044:Regression Analysis