  As the number of possible predictors generated by high-throughput experiments
continues to increase, methods are needed to quickly screen out unimportant
covariates. Model-based screening methods have been proposed and theoretically
justified, but only for a few specific models. Model-free screening methods
have also recently been studied, but can have lower power to detect important
covariates. In this paper we propose EEScreen, a screening procedure that can
be used with any model that can be fit using estimating equations, and provide
unified results on its finite-sample screening performance. EEScreen thus
generalizes many recently proposed model-based and model-free screening
procedures. We also propose iEEScreen, an iterative version of EEScreen, and
show that it is closely related to a recently studied boosting method for
estimating equations. We show via simulations for two different estimating
equations that EEScreen and iEEScreen are useful and flexible screening
procedures, and demonstrate our methods on data from a multiple myeloma study.
