A comparative study for the estimation of parameters in nonlinear models (Q1918308)
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scientific article; zbMATH DE number 911910
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | A comparative study for the estimation of parameters in nonlinear models |
scientific article; zbMATH DE number 911910 |
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A comparative study for the estimation of parameters in nonlinear models (English)
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5 January 1997
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The exponential regression is a relatively new noniterative method for estimating the parameters of nonlinear models, proposed by the first author [Appl. Math. Comput. 50, No. 1, 51-58 (1992; Zbl 0746.62061)]. The method of internal regression is another interesting method for estimating the parameters of nonlinear models which does not require the iterations. Based on the simple model \[ y= \theta_1(1- \theta_2 \exp(\theta_3 x)) \] and real data by \textit{H. O. Hartley} [Biometrika 35, 32-45 (1948; MR 10.50)], the authors compare their methods with Gauss-Newton, Newton-Raphson, Marquart and steepest descent methods and offer some general conclusions.
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parameter estimation
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nonlinear regression
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Gauss-Newton method
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Newton-Raphson method
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Marquardt method
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exponential regression
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noniterative method
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nonlinear models
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method of internal regression
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steepest descent methods
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0.752104640007019
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