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Nonparametric Regression via StatLSSVM

2013; Foundation for Open Access Statistics; Volume: 55; Issue: 2 Linguagem: Inglês

10.18637/jss.v055.i02

1548-7660

Kris De Brabanter, Johan A. K. Suykens, Bart De Moor,

Statistical Methods and Inference

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.