Bounded optimal knots for regression splines

Using a B-spline representation for splines with knots seen as free variables, the approximation to data by splines improves greatly. The main limitations are the presence of too many local optima in the univariate regression context, and it becomes even worse in multivariate additive modeling. When...

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Veröffentlicht in:	Computational statistics & data analysis Jg. 45; H. 2; S. 159 - 178
Hauptverfasser:	Molinari, Nicolas, Durand, Jean-François, Sabatier, Robert
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.03.2004 Elsevier Science Elsevier
Schriftenreihe:	Computational Statistics & Data Analysis
Schlagworte:	Additive models AIC BIC Bound constrained optimization Exact sciences and technology Free knots selection Linear inference, regression Mathematics Numerical analysis Numerical analysis. Scientific computation Numerical approximation Numerical methods in mathematical programming, optimization and calculus of variations Numerical methods in optimization and calculus of variations Probability and statistics Sciences and techniques of general use Statistics Surface estimation Free knots selection Surface estimation AIC Bound constrained optimization BIC Additive models B spline Statistical method Free knot selection Additive process Least squares method Regression spline AIC and BIC Spline approximation Constrained optimization
ISSN:	0167-9473, 1872-7352
Online-Zugang:	Volltext
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