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EVALUATING THE CMARS PERFORMANCE FOR MODELING NONLINEARITIES
Date
2010-02-04
Author
Batmaz, İnci
Kartal-Koc, Elcin
Köksal, Gülser
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Multivariate Adaptive Regression Splines (MARS) is a very popular nonparametric regression method particularly useful for modeling nonlinear relationships that may exist among the variables. Recently, we developed CMARS method as an alternative to backward stepwise part of the MARS algorithm. Comparative studies have indicated that CMARS performs better than MARS for modeling nonlinear relationships. In those studies, however, only main and two-factor interaction effects were sufficient to model the nonlinearity between the variables in the data sets. In this study, therefore, we aim at evaluating the model performances when there is a need for representing higher-order interaction effects in a nonlinear model. Results based on the comparison studies show that CMARS method performs better than MARS method according to most of the performance measures.
Subject Keywords
Multivariate adaptive regression splines
,
MARS
,
CMARS
,
Tikhonov regularization
,
Nonlinearity
,
Conic quadratic programming
URI
https://hdl.handle.net/11511/47034
DOI
https://doi.org/10.1063/1.3459772
Collections
Department of Statistics, Conference / Seminar
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Regression analysis is a widely used statistical method for modelling relationships between variables. Multivariate adaptive regression splines (MARS) especially is very useful for high-dimensional problems and fitting nonlinear multivariate functions. A special advantage of MARS lies in its ability to estimate contributions of some basis functions so that both additive and interactive effects of the predictors are allowed to determine the response variable. The MARS method consists of two parts: forward an...
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İ. Batmaz, E. Kartal-Koc, and G. Köksal, “EVALUATING THE CMARS PERFORMANCE FOR MODELING NONLINEARITIES,” 2010, vol. 1239, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47034.
