Model selection in multivariate adaptive regression splines (MARS) using information complexity as the fitness function

DOI10.1007/s10994-014-5440-5zbMath1383.62115OpenAlexW2137293424MaRDI QIDQ890291

Elcin Kartal Koc, Hamparsum Bozdogan

Publication date: 10 November 2015

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-014-5440-5

zbMATH Keywords

model selectionnonparametric regressioninformation complexitymultivariate adaptive regression splines (MARS)

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items (7)

Novel model selection criteria for LMARS: MARS designed for biological networks ⋮ Particle swarm optimization-based variable selection in Poisson regression analysis via information complexity-type criteria ⋮ Variable selection in linear regression analysis with alternative Bayesian information criteria using differential evaluation algorithm ⋮ A modified information criterion for model selection ⋮ Outlier detection in statistical modeling via multivariate adaptive regression splines ⋮ Estimating production functions through additive models based on regression splines ⋮ Fused Lasso penalized least absolute deviation estimator for high dimensional linear regression

Uses Software

• CMARS
• ElemStatLearn
• ARESLab

Cites Work

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