Methods to verify parameter equality in nonlinear regression models
DOI:
https://doi.org/10.1590/S0103-90162010000200014Keywords:
logistic model, treatments comparisonAbstract
In biologic experiments, in which growth curves are adjusted to sample data, treatments applied to the experimental material can affect the parameter estimates. In these cases the interest is to compare the growth functions, in order to distinguish treatments. Three methods that verify the equality of parameters in nonlinear regression models were compared: (i) developed by Carvalho in 1996, performing ANOVA on estimates of parameters of individual fits; (ii) suggested by Regazzi in 2003, using the likelihood ratio method; and (iii) constructing a pooled variance from individual variances. The parametric tests, F and Tukey, were employed when the parameter estimators were near to present the properties of linear model estimators, that is, unbiasedness, normal distribution and minimum variance. The first and second methods presented similar results, but the third method is simpler in calculations and uses all information contained in the original data.Downloads
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Published
2010-04-01
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Section
Statistics
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All content of the journal, except where identified, is licensed under a Creative Common attribution-type BY-NC.How to Cite
Methods to verify parameter equality in nonlinear regression models . (2010). Scientia Agricola, 67(2), 218-222. https://doi.org/10.1590/S0103-90162010000200014