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Transformed Tree-Structured Regression Method

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Abstract:

Many times the response variable is linked linearly to the function of the regressors and to the error term through its function g(Y). For this reason the traditional tree-structured regression methods do not understand the real relationship between the regressors and the dependent variable. I derive a modified version of the most popular tree-structured regression methods to consider this situation of nonlinearity. My simulation results show that my method with regression tree is better than the tree-based regression methods proposed in literature because it understands the true relationship between the regressors and the dependent variable also when it is not possible to divide exactly the error part from the regressors part.

Info:

Periodical:
Bulletin of Mathematical Sciences and Applications (Volume 16)
Pages:
70-75
DOI:
https://doi.org/10.18052/www.scipress.com/BMSA.16.70
Citation:
G. Gheno "Transformed Tree-Structured Regression Method", Bulletin of Mathematical Sciences and Applications, Vol. 16, pp. 70-75, 2016
Online since:
Aug 2016
Authors:
Gloria Gheno
Keywords:
Birckel-Docksum Transformation, Nonlinearity, Random Forest, Regression Tree
Export:
RIS, BibTeX, Text
Distribution:
Open Access

Creative Commons License
This work is licensed under a
Creative Commons Attribution 4.0 International License

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