A convex version of multivariate adaptive regression splines

DOI10.1016/j.csda.2014.07.015OpenAlexW2087613562MaRDI QIDQ1623730

Diana L. Martinez, Dachuan T. Shih, Victoria C. P. Chen, Seoung Bum Kim

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2014.07.015

zbMATH Keywords

convexityregression splines

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08)

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Cites Work

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• Greedy function approximation: A gradient boosting machine.
• A multivariate adaptive regression splines cutting plane approach for solving a two-stage stochastic programming fleet assignment model
• Pricing a nontradeable asset and its derivatives.
• Efficient computer experiment-based optimization through variable selection
• Neural network and regression spline value function approximations for stochastic dynamic programming
• Multivariate adaptive regression splines
• Application of orthogonal arrays and MARS to inventory forecasting stochastic dynamic programs.
• Design and analysis of computer experiments. With comments and a rejoinder by the authors
• Stochastic dynamic programming formulation for a wastewater treatment decision-making framework
• Applying Experimental Design and Regression Splines to High-Dimensional Continuous-State Stochastic Dynamic Programming
• Flexible Parsimonious Smoothing and Additive Modeling
• Design and analysis of simulation experiments