An Iterative Projection Algorithm and Some Simulation Results

. An iterative projection method for large linear equation systems is described. It has favourable properties with respect to many statistical applications. A major advantage is that convergence can be established without restrictions on the system matrix. Hence diagonal dominance or regularity are not required. The reason why this numerical method has not been much used in computational statistics is its slow convergence behaviour. In this paper we introduce a relaxation concept and the optimal choice of the relaxation parameter, even for nearly singular systems, is studied in a simulation experiment. Keywords. Iterative projections, lack of diagonal dominance, linear equation systems, non-parametrics, regression, relaxation, regression fitting, singularity, simulations, time series fitting 1 Introduction The estimation of many parametric, non- as well as semiparametric statistical models involves the solution of large linear equation systems. Up to now iterative procedures like Jac..