Performance criteria and discrimination of extreme undersmoothing in nonparametric regression

DOI10.1016/j.jspi.2014.05.006zbMath1365.62141OpenAlexW1992833554MaRDI QIDQ2250696

Mark A. Lukas

Publication date: 21 July 2014

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: http://researchrepository.murdoch.edu.au/id/eprint/23145/

zbMATH Keywords

prediction errorkernel smoothingsmoothing splineoptimality criterionSobolev erroraverage squared error

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07)

Related Items (2)

Asymptotic expansions for the incomplete gamma function in the transition regions ⋮ Practical use of robust GCV and modified GCV for spline smoothing

Uses Software

• KernSmooth
• DLMF
• GCVPACK

Cites Work

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