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Nonparametric Regression via StatLSSVM
Journal of Statistical Software
  • Kris De Brabanter, Iowa State University
  • Johan AK Suykens, KU Leuven
  • Bart De Moor, KU Leuven
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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.

This article is from Journal of Statistical Software 55 (2013): 1, doi: 10.18637/jss.v055.i02. Posted with permission.

Copyright 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.
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Kris De Brabanter et al
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Kris 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
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