L1-regularization path algorithm for generalized linear models

We introduce a path following algorithm for L-1-regularized generalized linear models. The L-1-regularization procedure is useful especially because it, in effect, selects variables according to the amount of penalization on the L-1-norm of the coefficients, in a manner that is less greedy than forward selection-backward deletion. The generalized linear model path algorithm efficiently computes solutions along the entire regularization path by using the predictor-corrector method of convex optimization. Selecting the step length of the regularization parameter is critical in controlling the overall accuracy of the paths; we suggest intuitive and flexible strategies for choosing appropriate values. We demonstrate the implementation with several simulated and real data sets.