Efficient adaptive regression spline algorithms based on mapping approach with a case study on finance
DOI10.1007/s10898-014-0211-1zbMath1308.65018OpenAlexW2090225957MaRDI QIDQ740643
Cem Iyigun, Elcin Kartal Koc, Gerhard-Wilhelm Weber, İnci Batmaz
Publication date: 4 September 2014
Published in: Journal of Global Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10898-014-0211-1
optimizationdata miningregression splinesCMARSmultivariate adaptive regression splines (MARS)interest rate estimationself organizing maps
Lua error in Module:PublicationMSCList at line 37: attempt to index local 'msc_result' (a nil value).
Related Items (2)
Uses Software
Cites Work
- Unnamed Item
- CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization
- RCMARS: robustification of CMARS with different scenarios under polyhedral uncertainty set
- Restructuring forward step of MARS algorithm using a new knot selection procedure based on a mapping approach
- A computational approach to nonparametric regression: bootstrapping CMARS method
- Multivariate adaptive regression splines
- Self-organization and associative memory.
- Polynomial splines and their tensor products in extended linear modeling. (With discussions)
- Mining the customer credit using classification and regression tree and multivariate adaptive regression splines
- Bayesian curve-fitting with free-knot splines
- Flexible Parsimonious Smoothing and Additive Modeling
- Automatic Bayesian Curve Fitting
- Hybrid Adaptive Splines
- Spatially Adaptive Regression Splines and Accurate Knot Selection Schemes
- New approaches to regression by generalized additive models and continuous optimization for modern applications in finance, science and technology
This page was built for publication: Efficient adaptive regression spline algorithms based on mapping approach with a case study on finance