Nonparametric Regression via StatLSSVMJournal of Statistical Software
Publication VersionPublished Version
AbstractWe 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.
RightsCopyright 2013 Kris De Brabanter et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright OwnerKris De Brabanter et al
Citation InformationKris De Brabanter, Johan AK Suykens and Bart De Moor. "Nonparametric Regression via StatLSSVM" Journal of Statistical Software Vol. 55 Iss. 2 (2013) p. 1 - 21
Available at: http://works.bepress.com/kris_debrabanter/4/