nonlinear regression


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nonlinear regression

[′nän‚lin·ē·ər ri′gresh·ən]
(statistics)
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For being able to describe the most diverse situations, to present better quality of fit even using few parameters and, mainly, to present practical interpretation for the estimations of these parameters, the nonlinear regression models are very used in the most diverse areas of knowledge, mainly in the study of growth curves (THOLON & QUEIROZ, 2009; PEREIRA et al.
To select an appropriate growth model we first considered several age-based models that have been reparameterized for size-based application to tag-recapture data, including von Bertalanffy, Gompertz, and Schnute growth models, as described by Francis (1995), and a nonlinear regression model described for sablefish by Kimura et al.
In this situation, the parameters which were obtained from nonlinear regression analyses used the incomplete growth data may be incorrect.
Harvey Motulsky and Arthur Christopoulos9 concludes "Most models in biology are nonlinear, so many biologist use nonlinear regression more often than linear regression.
Such mean estimands are widely used in health services research when predicting average treatment effects from logistic, Poisson, and other nonlinear regression models.
The use of the function r instead of an approximation to p(x) allows reducing isoconversional and nonisoconversional integral analysis of data to a nonlinear regression of parameters.
A number of methods are used to approximate experimental data, including hierarchical nonlinear approximation [8], Wavelet Transform [9], approximation by piecewise cubic spline method [10], using linear and nonlinear regression [11], etc.
The older techniques include polynomial regression, linear regression with nonlinear terms, and nonlinear regression.
The idea of SVR for nonlinear regression is to build a mapping x [right arrow] [phi](x) from the original m dimension feature space X to a new feature space X' whose dimension depends on the mapping scheme and is not necessarily finite.
The analysis of repeated measures is based on linear and nonlinear regression models, which incorporate correlation between observations on the same individual.
This type of curve generally has a sigmoid al appearance (S-shaped) well adjusted by nonlinear regression.
where SD is the standard deviation of the EQA results, c is the concentration of the analyte; and [alpha] and [beta] are coefficients that have to be estimated via a nonlinear regression model.

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