N-stage splitting for maximum penalized likelihood estimation with Gaussian data and stationary linear iterative methods
By interpreting N-stage splitting for the maximum penalized likelihood estimation problem with Gaussian data (ridge regression problem) as a stationary linear iterative method, the convergence behaviour for varying values of the smoothing (ridge) parameter is explained. Given a statistically based choice of the smoothing parameter, it is shown, under restricted conditions, how to choose the splitting order N which is optimal for convergence. Simulation evidence for the robustness of this choice rule for N is also considered. Consequently, N-SS provides enhanced computational speed without compromising the quality of the ridge estimate.
maximum penalized likelihood estimation ridge regression splitting algorithms