  In this article, we investigate the properties of the EBIC in variable
selection for generalized linear models with non-canonical links and diverging
number of parameters in ultra-high dimensional feature space. The selection
consistency of the EBIC in this situation is established under moderate
conditions. The finite sample performance of the EBIC coupled with a forward
selection procedure is demonstrated through simulation studies and a real data
analysis.
