On wavelets to select the parametric form of a regression model

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...

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Bibliographic Details
Published in:arXiv.org 2018-09
Main Authors: Eufrásio de A Lima Neto, Aluísio de S Pinheiro, Ferreira, Adenice G O
Format: Article
Language:English
Subjects:
Generalized linear models
Parametric equations
Regression analysis
Regression models
Statistical analysis
Statistical models
Wavelet analysis
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.1809.11064