Sequential Feature Screening for Generalized Linear Models with Sparse Ultra-High Dimensional Data

This paper considers the iterative sequential lasso(ISLasso)variable selection for generalized linear model with ultrahigh dimensional feature space.The ISLasso selects features by estimated parameter sequentially iteratively for the second order approximation of likelihood function where the features selected depend on regulatory parameters.The procedure stops when extended BIC(EBIC)reaches a minimum.Simulation study demonstrates that the new method is a desirable approach over other methods.