On the Assumption of Bivariate Normality in Selection Models A Copula Approach Applied to Estimating HIV Prevalence

摘要

Background: Heckman-type selection models have been used to control HIV prevalence estimates for selection bias when participation in HIV testing and HIV status are associated after controlling for observed variables. These models typically rely on the strong assumption that the error terms in the participation and the outcome equations that comprise the model are distributed as bivariate normal.