Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Kris De Brabanter, Johan Suykens, Bart De Moor
Title: Nonparametric Regression via StatLSSVM
Abstract: We present a new MATLAB toolbox under Windows and Linux for nonparametric regression estimation based on the statistical library for least squares support vector machines (StatLSSVM). The StatLSSVM toolbox is written so that only a few lines of code are necessary in order to perform standard nonparametric regression, regression with correlated errors and robust regression. In addition, construction of additive models and pointwise or uniform confidence intervals are also supported. A number of tuning criteria such as classical cross-validation, robust cross-validation and cross-validation for correlated errors are available. Also, minimization of the previous criteria is available without any user interaction.

Page views:: 3595. Submitted: 2011-08-29. Published: 2013-10-22.
Paper: Nonparametric Regression via StatLSSVM     Download PDF (Downloads: 3402)
Supplements:
StatLSSVM.zip: MATLAB source package Download (Downloads: 455; 310KB)
StatLSSVM-manual.pdf: StatLSSVM user manual Download (Downloads: 1234; 800KB)
v55i02.m: MATLAB example code from the paper Download (Downloads: 579; 5KB)

DOI: 10.18637/jss.v055.i02

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.