  We propose an iterative variable selection scheme for high-dimensional data
with binary outcomes. The scheme adopts a structured screen-and-select
framework and uses non-local prior-based Bayesian model selection within the
same. The structured screening is based on the association of the independent
variables with the outcome which is measured in terms of the maximum marginal
likelihood estimator. Performance comparison with several well-known methods in
terms of true positive rate and false discovery rate shows that our proposed
method stands to be a competitive alternative for sparse high-dimensional
variable selection with binary outcomes. The method has been implemented within
the R package GWASinlps.
