A data-adaptive knot selection scheme for fitting splines

Xuming He; Lixin Shen; Zuowei Shen

doi:10.1109/97.917695

A data-adaptive knot selection scheme for fitting splines

Xuming He, Lixin Shen, Zuowei Shen

Research output: Contribution to journal › Article › peer-review

26 Scopus citations

Abstract

A critical component of spline smoothing is the choice of knots, especially for curves with varying shapes and frequencies in its domain. We consider a two-stage knot selection scheme for adaptively fitting splines to data subject to noise. A potential set of knots is chosen based on information from certain wavelet decompositions with the intention of placing more points where the curve shows rapid changes. The final knot selection is then made based on statistical model selection ideas. We show that the proposed method is well suited for a variety of smoothing and noise filtering needs.

Original language	English (US)
Pages (from-to)	137-139
Number of pages	3
Journal	IEEE Signal Processing Letters
Volume	8
Issue number	5
DOIs	https://doi.org/10.1109/97.917695
State	Published - May 2001
Externally published	Yes

Keywords

Knot
Least squares
Model selection
Smoothing
Spline
Wavelet decomposition

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

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10.1109/97.917695

Cite this

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title = "A data-adaptive knot selection scheme for fitting splines",

abstract = "A critical component of spline smoothing is the choice of knots, especially for curves with varying shapes and frequencies in its domain. We consider a two-stage knot selection scheme for adaptively fitting splines to data subject to noise. A potential set of knots is chosen based on information from certain wavelet decompositions with the intention of placing more points where the curve shows rapid changes. The final knot selection is then made based on statistical model selection ideas. We show that the proposed method is well suited for a variety of smoothing and noise filtering needs.",

keywords = "Knot, Least squares, Model selection, Smoothing, Spline, Wavelet decomposition",

author = "Xuming He and Lixin Shen and Zuowei Shen",

note = "Funding Information: Manuscript received December 27, 2000. This work was supported in part by the Wavelets Strategic Research Program, National University of Singapore, Singapore. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. A. Petropulu. X. He is with the Department of Statistics, University of Illinois, Champaign, IL 61820 USA (e-mail: [email protected]). L. Shen and Z. Shen are with the Department of Mathematics, National University of Singapore, Singapore (e-mail: [email protected]). Publisher Item Identifier S 1070-9908(01)03289-8.",

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AU - Shen, Lixin

AU - Shen, Zuowei

N1 - Funding Information: Manuscript received December 27, 2000. This work was supported in part by the Wavelets Strategic Research Program, National University of Singapore, Singapore. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. A. Petropulu. X. He is with the Department of Statistics, University of Illinois, Champaign, IL 61820 USA (e-mail: [email protected]). L. Shen and Z. Shen are with the Department of Mathematics, National University of Singapore, Singapore (e-mail: [email protected]). Publisher Item Identifier S 1070-9908(01)03289-8.

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N2 - A critical component of spline smoothing is the choice of knots, especially for curves with varying shapes and frequencies in its domain. We consider a two-stage knot selection scheme for adaptively fitting splines to data subject to noise. A potential set of knots is chosen based on information from certain wavelet decompositions with the intention of placing more points where the curve shows rapid changes. The final knot selection is then made based on statistical model selection ideas. We show that the proposed method is well suited for a variety of smoothing and noise filtering needs.

AB - A critical component of spline smoothing is the choice of knots, especially for curves with varying shapes and frequencies in its domain. We consider a two-stage knot selection scheme for adaptively fitting splines to data subject to noise. A potential set of knots is chosen based on information from certain wavelet decompositions with the intention of placing more points where the curve shows rapid changes. The final knot selection is then made based on statistical model selection ideas. We show that the proposed method is well suited for a variety of smoothing and noise filtering needs.

KW - Knot

KW - Least squares

KW - Model selection

KW - Smoothing

KW - Spline

KW - Wavelet decomposition

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A data-adaptive knot selection scheme for fitting splines

Abstract

Keywords

ASJC Scopus subject areas

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