Nonparametric Regression via StatLSSVM

Nonparametric Regression via StatLSSVM

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 nonpara...

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Bibliographic Details
Main Authors: Kris De Brabanter, Johan Suykens, Bart De Moor
Format: Article
Language:English
Published: Foundation for Open Access Statistics 2013-10-01
Series:Journal of Statistical Software
Online Access:http://www.jstatsoft.org/index.php/jss/article/view/2097
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spelling doaj-f59be64b37c84931b4fcc066f898fe872020-11-24T22:01:47ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602013-10-0155112110.18637/jss.v055.i02701Nonparametric Regression via StatLSSVMKris De BrabanterJohan SuykensBart De MoorWe 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.http://www.jstatsoft.org/index.php/jss/article/view/2097
collection DOAJ
language English
format Article
sources DOAJ
author Kris De Brabanter
Johan Suykens
Bart De Moor
spellingShingle Kris De Brabanter
Johan Suykens
Bart De Moor
Nonparametric Regression via StatLSSVM
Journal of Statistical Software
author_facet Kris De Brabanter
Johan Suykens
Bart De Moor
author_sort Kris De Brabanter
title Nonparametric Regression via StatLSSVM
title_short Nonparametric Regression via StatLSSVM
title_full Nonparametric Regression via StatLSSVM
title_fullStr Nonparametric Regression via StatLSSVM
title_full_unstemmed Nonparametric Regression via StatLSSVM
title_sort nonparametric regression via statlssvm
publisher Foundation for Open Access Statistics
series Journal of Statistical Software
issn 1548-7660
publishDate 2013-10-01
description 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.
url http://www.jstatsoft.org/index.php/jss/article/view/2097
work_keys_str_mv AT krisdebrabanter nonparametricregressionviastatlssvm
AT johansuykens nonparametricregressionviastatlssvm
AT bartdemoor nonparametricregressionviastatlssvm
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