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- W2893797783 abstract "Let Y be a response variable related with a set of explanatory variables and let f1, f2, ..., fk be a set of the parametric forms representing a set of candidate's model. Let f* be the true model among the set of k plausible models. We discuss in this paper the use of wavelet regression method as auxiliary for the choice of the true parametric form of a regression model, particularly, for the cases of nonlinear regression and generalized linear models. The use of a non-parametric method for the choice of the more appropriate parametric equation in regression problems would be interesting in practice due to the simplicity and because the probabilistic assumptions are not required. We evaluate the performance of the proposed wavelet procedure based on the true classification rate of the correct parametric form among a range of k candidate models, taking into account a wide ranges of scenarios and configurations as well as in real data set applications." @default.
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- W2893797783 date "2019-05-12" @default.
- W2893797783 modified "2023-09-30" @default.
- W2893797783 title "On wavelet to select the parametric form of a regression model" @default.
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- W2893797783 doi "https://doi.org/10.1080/03610918.2019.1610441" @default.
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