  We study a class of missingness mechanisms, called sequentially additive
nonignorable, for modeling 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 present theory proving identification results, and illustrate the
use of these mechanisms in an application.
