Abstract  An informal discussion is given on performing an unconstrained maximization or solving non‐linear equations of statistics by iterative methods with the quadratic termination property. It is shown that if a miximized function, e.g. likelihood, is asymptotically quadratic, then for asymptotically efficient inference finitely many iterations are needed. Copyright

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Keywords asymptotically differentiable, asymptotically quadratic, conjugate gradient, Davidon‐Fletcher‐Powell, methods of Newton‐Raphson, quadratic termination, quasi‐Newton, scoring
Persistent URL dx.doi.org/10.1111/j.1467-9574.1983.tb00813.x
Journal Statistica Neerlandica
Citation
Dzhaparidze, K.O. (1983). On iterative procedures of asymptotic inference. Statistica Neerlandica, 37(4), 181–189. doi:10.1111/j.1467-9574.1983.tb00813.x