A Bayesian method for addressing multinomial misclassification with applications for alcohol epidemiological modeling

Abstract

The purpose of this paper is to describe a new Stata command, bamm,
which implements a Bayesian method for addressing misclassification in multinomial data; see Swartz et al. (2004). We also describe a post-estimation command, bammdx, which was developed to provide additional estimation diagnostics. We describe the method and the new Stata commands, and then present results from both a simulation study demonstrating bamm’s performance under a known misclassification data generating process and an empirical example from alcohol epidemiology modeling.