A new approach to multivariate adaptive regression splines by using Tikhonov regularization and continuous optimization

DOI10.1007/s11750-010-0155-7zbMath1208.41007OpenAlexW2078100786MaRDI QIDQ621735

Fatma Yerlikaya Özkurt, Pakize Taylan, Gerhard-Wilhelm Weber

Publication date: 28 January 2011

Published in: Top (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11750-010-0155-7

zbMATH Keywords

classificationclusteringcurvatureinterior point methodsregressionpenalty methodsMARSstatistical learningcontinuous optimizationconic quadratic programmingwell-structured convex problems

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ill-posedness and regularization problems in numerical linear algebra (65F22) Interior-point methods (90C51) Spline approximation (41A15) Applications of operator theory in optimization, convex analysis, mathematical programming, economics (47N10)

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Uses Software

• Regularization tools
• UCI-ml
• UTV

Cites Work

• Optimization of gene-environment networks in the presence of errors and uncertainty with Chebychev approximation
• Multivariate adaptive regression splines
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• Regularization tools version \(4.0\) for matlab \(7.3\)
• New approaches to regression by generalized additive models and continuous optimization for modern applications in finance, science and technology
• The elements of statistical learning. Data mining, inference, and prediction
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