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SALSA – A Spatially Adaptive Local Smoothing Algorithm

Research output: Research - peer-reviewArticle

DOI

Standard

SALSA – A Spatially Adaptive Local Smoothing Algorithm. / Walker, Cameron; MacKenzie, Monique Lea; Donovan, Carl Robert; O'Sullivan, M.

In: Journal of Statistical Computation and Simulation, Vol. 81, No. 2, 02.2011, p. 179-191.

Research output: Research - peer-reviewArticle

Harvard

Walker, C, MacKenzie, ML, Donovan, CR & O'Sullivan, M 2011, 'SALSA – A Spatially Adaptive Local Smoothing Algorithm' Journal of Statistical Computation and Simulation, vol 81, no. 2, pp. 179-191. DOI: 10.1080/00949650903229041

APA

Walker, C., MacKenzie, M. L., Donovan, C. R., & O'Sullivan, M. (2011). SALSA – A Spatially Adaptive Local Smoothing Algorithm. Journal of Statistical Computation and Simulation, 81(2), 179-191. DOI: 10.1080/00949650903229041

Vancouver

Walker C, MacKenzie ML, Donovan CR, O'Sullivan M. SALSA – A Spatially Adaptive Local Smoothing Algorithm. Journal of Statistical Computation and Simulation. 2011 Feb;81(2):179-191. Available from, DOI: 10.1080/00949650903229041

Author

Walker, Cameron ; MacKenzie, Monique Lea ; Donovan, Carl Robert ; O'Sullivan, M. / SALSA – A Spatially Adaptive Local Smoothing Algorithm. In: Journal of Statistical Computation and Simulation. 2011 ; Vol. 81, No. 2. pp. 179-191

Bibtex - Download

@article{eabc16a29caa4976a73041a037d36f20,
title = "SALSA – A Spatially Adaptive Local Smoothing Algorithm",
abstract = "We present a nonlinear integer programming formulation for fitting a spline-based regression to 2-dimensional data using an adaptive knot-selection approach, with the number and location of the knots being determined in the solution process. However, the nonlinear nature of this formulation makes its solution impractical, so we also outline a knot selection heuristic inspired by the Remes Exchange Algorithm, to produce good solutions to our formulation. This algorithm is intuitive and naturally accommodates local changes in smoothness. Results are presented for the algorithm demonstrating performance that is as good, or better, than other current methods on established benchmark functions.",
author = "Cameron Walker and MacKenzie, {Monique Lea} and Donovan, {Carl Robert} and M O'Sullivan",
year = "2011",
month = "2",
doi = "10.1080/00949650903229041",
volume = "81",
pages = "179--191",
journal = "Journal of Statistical Computation and Simulation",
issn = "0094-9655",
publisher = "Taylor and Francis",
number = "2",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - SALSA – A Spatially Adaptive Local Smoothing Algorithm

AU - Walker,Cameron

AU - MacKenzie,Monique Lea

AU - Donovan,Carl Robert

AU - O'Sullivan,M

PY - 2011/2

Y1 - 2011/2

N2 - We present a nonlinear integer programming formulation for fitting a spline-based regression to 2-dimensional data using an adaptive knot-selection approach, with the number and location of the knots being determined in the solution process. However, the nonlinear nature of this formulation makes its solution impractical, so we also outline a knot selection heuristic inspired by the Remes Exchange Algorithm, to produce good solutions to our formulation. This algorithm is intuitive and naturally accommodates local changes in smoothness. Results are presented for the algorithm demonstrating performance that is as good, or better, than other current methods on established benchmark functions.

AB - We present a nonlinear integer programming formulation for fitting a spline-based regression to 2-dimensional data using an adaptive knot-selection approach, with the number and location of the knots being determined in the solution process. However, the nonlinear nature of this formulation makes its solution impractical, so we also outline a knot selection heuristic inspired by the Remes Exchange Algorithm, to produce good solutions to our formulation. This algorithm is intuitive and naturally accommodates local changes in smoothness. Results are presented for the algorithm demonstrating performance that is as good, or better, than other current methods on established benchmark functions.

UR - http://www.scopus.com/inward/record.url?scp=78650262267&partnerID=8YFLogxK

U2 - 10.1080/00949650903229041

DO - 10.1080/00949650903229041

M3 - Article

VL - 81

SP - 179

EP - 191

JO - Journal of Statistical Computation and Simulation

T2 - Journal of Statistical Computation and Simulation

JF - Journal of Statistical Computation and Simulation

SN - 0094-9655

IS - 2

ER -

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ID: 326858