The aim of this paper is to describe a simulation procedure to compare parametric regression against a non-parametric regression method, for different functions and sets of information. The proposed methodology improves lack of fit at the edges of the regression curves, and an acceptable result is obtained for the no-parametric estimation in all studied cases. Larger differences appear at the edges of the estimation. The results are applied to the study of dasometric variables, which do not fulfil the normality hypothesis needed for parametric estimation. The kernel regression shows the relationship between the studied variables, which would not be detected with more rigid parametric models.Regression kernel, edge effect, simulation, comparison, dasometric variables,
A procedure to choose the best non-parametric estimator from among all nonparametric methods to fit regression curves is described. The methodology that is proposed prevents a lack of fit at the edges of the regression curve. The method is summed up in a few steps to facilitate its application by researchers. The procedure is applied to the determination of various curves that explain the anti-inflammatory activity of diverse extracts of Sideritis foetens and phenylbutazone against the time elapsed from the application of the agent which provokes the inflammation. Discussion shows that it is possible to obtain valid conclusions about the effects of the different products and to establish comparisons between them. Such conclusions are not possible when starting from the classical statistics methods usually employed in pharmacology.
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