Nonparametric Regression via StatLSSVM

Kris De Brabanter, Johan Suykens and Bart De Moor

Journal of Statistical Software, 2013, vol. 055, issue i02

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.

Date: 2013-10-22
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.jstatsoft.org/index.php/jss/article/view/v055i02/v55i02.pdf
https://www.jstatsoft.org/index.php/jss/article/do ... 055i02/StatLSSVM.zip
https://www.jstatsoft.org/index.php/jss/article/do ... StatLSSVM-manual.pdf
https://www.jstatsoft.org/index.php/jss/article/do ... ile/v055i02/v55i02.m

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:055:i02

DOI: 10.18637/jss.v055.i02

Access Statistics for this article

Journal of Statistical Software is currently edited by Bettina Grün, Edzer Pebesma and Achim Zeileis

More articles in Journal of Statistical Software from Foundation for Open Access Statistics
Bibliographic data for series maintained by Christopher F. Baum ().