Walker, Homer F.2023-10-042023-10-041987-03-10198615372593https://hdl.handle.net/10657/15238A quasi-Newton algorithm with an adaptive global convergence scheme is used for numerical maximum likelihood estimates (MLE). For this method, which uses a DFP-type Hessian update for unconstrained minimization, q-superlinear local convergence is shown. We discuss this in the context of a broader class of algorithms, among them R. A. Fisher's method of scoring and the EM algorithm. A computer code, MLESOL, is described and applied to the mixture density problem.application/pdfenThis item is protected by copyright but is made available here under a claim of fair use (17 U.S.C. Section 107) for non-profit research and educational purposes. Users of this work assume the responsibility for determining copyright status prior to reusing, publishing, or reproducing this item for purposes other than what is allowed by fair use or other copyright exemptions. Any reuse of this item in excess of fair use or other copyright exemptions requires express permission of the copyright holder.Nonlinear theoriesMixture distributions (Probability theory)Thesisreformatted digital