Binary models with misclassification in the variable of interest and nonignorable missing data

Abstract

In this paper we propose a general framework to deal with datasets where a binary outcome is subject to misclassification and, for some sampling units, neither the error-prone variable of interest nor the covariates are recorded. A model to describe the observed data is formalized and efficient likelihood-based generalized method of moments estimators are suggested.