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SALSA – A Spatially Adaptive Local Smoothing Algorithm

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Author(s)

Cameron Walker, Monique Lea MacKenzie, Carl Robert Donovan, M O'Sullivan

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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.
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Details

Original languageEnglish
Pages (from-to)179-191
JournalJournal of Statistical Computation and Simulation
Volume81
Issue number2
Early online date9 Mar 2010
DOIs
Publication statusPublished - Feb 2011

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