A New Algorithm in Maximum Likelihood Estimation for Generalized Linear Models

Abstract

We intrduce a new algorithm for  regularized generalized linear models. The  regularization procedure is useful,especially because it ,in effect,selects variables according to the amount of penalization on the  norm of the coefficients,in a manner less greedy than forward selection/backward deletion. The algorithm efficiently computes solutions along the entire regularization path 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.