Fisher's Method of Scoring

Abstract

Summary An analysis is given of the computational properties of Fisher's method of scoring for maximizing likelihoods and solving estimating equations based on quasi-likelihoods. Consistent estimation of the true parameter vector is shown to be important if a fast rate of convergence is to be achieved, but if this condition is met then the algorithm is very attractive. This link between the performance of the scoring algorithm and the adequacy of the underlying problem modelling is stressed. The effect of linear constraints on performance is discussed, and examples of likelihood and quasi-likelihood calculations are presented.