Sequentially additive nonignorable missing data modelling using auxiliary marginal information

We study a class of missingness mechanisms, referred to as sequentially additive nonignorable, for modelling multivariate data with item nonresponse. These mechanisms explicitly allow the probability of nonresponse for each variable to depend on the value of that variable, thereby representing nonignorable missingness mechanisms. These missing data models are identified by making use of auxiliary information on marginal distributions, such as marginal probabilities for multivariate categorical variables or moments for numeric variables.We prove identification results and illustrate the use of these mechanisms in an application.

Duke Scholars

Author Jerome P. Reiter Statistical Science