Using recursive algorithms for the efficient identification of smoothing spline ANOVA models

DOI10.1007/s10182-010-0148-8zbMath1477.62098OpenAlexW1998515634WikidataQ56385626 ScholiaQ56385626MaRDI QIDQ2006880

Marco Ratto, Andrea Pagano

Publication date: 12 October 2020

Published in: AStA. Advances in Statistical Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10182-010-0148-8

zbMATH Keywords

sensitivity analysisrecursive algorithmsbackfittingsmoothing spline ANOVA models

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Analysis of variance and covariance (ANOVA) (62J10)

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Uses Software

• DACE
• gss

Cites Work

• Component selection and smoothing in multivariate nonparametric regression
• A stochastic framework for recursive computation of spline functions. II: Smoothing splines
• Computer experiments: a review
• Modelling of a thermomechanically coupled forming process based on functional outputs from a finite element analysis and from experimental measurements
• Surface estimation, variable selection, and the nonparametric oracle property
• The Signal Extraction Approach to Nonlinear Regression and Spline Smoothing
• Sensitivity measures,anova-like Techniques and the use of bootstrap
• Recursive and en-bloc approaches to signal extraction
• Evaluation of likelihood functions for Gaussian signals
• Smoothing spline ANOVA models
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