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- W1598230356 abstract "Least squares optimization offers an objective function which, compared to other computational strategies, is favourable for non-linear models. Generally, iterative procedures using approximations of the objective function are used to find the global minimum and its parameter set. Convergence is a crucial aspect in these calculations. In this respect it is not always realized that for a specific model many model functions can usually be raised, each exhibiting a different degree of non-linearity and, on substitution in the objective function, an associated ease of convergence. For good convergence, a close-to-linear model function with a low intrinsic non-linearity should be selected. The remaining parameter non-linearity can be decreased by change of parameters. The measures of non-linearity are elucidated geometrically in the sample space in relation to possible non-Gaussian behaviour and bias of the estimated parameters." @default.
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- W1598230356 date "1986-01-01" @default.
- W1598230356 modified "2023-10-18" @default.
- W1598230356 title "Non-linear multiparameter estimation in spectrophotometry" @default.
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- W1598230356 doi "https://doi.org/10.1016/0003-2670(86)80025-3" @default.
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