Smoothing spline regression estimation based on real and artificial data

Dmytro Furer () and Michael Kohler ()

Metrika: International Journal for Theoretical and Applied Statistics, 2015, vol. 78, issue 6, 746 pages

Abstract: In this article we introduce a smoothing spline estimate for fixed design regression estimation based on real and artificial data, where the artificial data comes from previously undertaken similar experiments. The smoothing spline estimate gives different weights to the real and the artificial data. It is investigated under which conditions the rate of convergence of this estimate is better than the rate of convergence of the ordinary smoothing spline estimate applied to the real data only. The finite sample size performance of the estimate is analyzed using simulated data. The usefulness of the estimate is illustrated by applying it in the context of experimental fatigue tests. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Fixed design regression; Nonparametric estimation; $$L_2$$ L 2 error; Rate of convergence; Smoothing spline; 62G08; 62G20 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:metrik:v:78:y:2015:i:6:p:711-746

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DOI: 10.1007/s00184-014-0524-6

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