Abstract
A class of fast convergent iteration procedures for ML factor analysis is presented in this paper. It includes a further development of Joreskog’s (1971 with van Thillo, 1977) Newton-Raphson-like procedure which is widely available in statistical program packages but which is inclined to fail when solving difficult problems. In a comparison of efficiency, besides these two algorithms, our own versions of two quasi-Newton methods, namely the Davidon-Fletcher-Powell (DFP) and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method, are tested.
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