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- W3022164664 abstract "Publisher SummaryA usual way of estimating the parameters of a statistical model is to seek the values that maximize or minimize a criterion function, such as a likelihood function or a sum of squares of prediction errors. If the criterion function is a quadratic function of the unknown parameters, then the first-order conditions for its optimization gives rise to a set of linear estimating equations which are easily solved to obtain the estimates. An approach which is aimed at solving the first-order conditions can take account of the particular features of the problem at hand. An optimization approach, in contrast, must rely upon a general theory of nonlinear functions. If an optimization technique is to be widely applicable, then it must be capable of dealing with all contingencies; and, therefore, robust methods tend to be complex. This chapter considers only the minimization of functions, because a problem of maximization can be solved by minimizing the negative of the function in question. The functions are assumed to be continuous and smooth, which means that they must be twice-differentiable." @default.
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- W3022164664 date "1999-01-01" @default.
- W3022164664 modified "2023-09-23" @default.
- W3022164664 title "Unconstrained Optimisation" @default.
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- W3022164664 doi "https://doi.org/10.1016/b978-012560990-6/50014-2" @default.
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