Nonparametric Regression via StatLSSVM

被引：0

作者：

De Brabanter, Kris ^{[1
]}

Suykens, Johan A. K. ^{[2
]}

De Moor, Bart ^{[3
]}

机构：

[1] Iowa State Univ, Dept Stat & Comp Sci, Ames, IA 50011 USA

[2] Katholieke Univ Leuven, Dept Elect Engn ESAT STADIUS, B-3001 Louvain, Belgium

[3] Katholieke Univ Leuven, ESAT STADIUS, iMinds Future Hlth, B-3001 Louvain, Belgium

来源：

JOURNAL OF STATISTICAL SOFTWARE | 2013年 / 55卷 / 02期

基金：

欧洲研究理事会;

关键词：

nonparametric regression; pointwise confidence interval; uniform confidence interval; volume-of-tube-formula; asymptotic normality; robustness; reweighting; correlated error; bimodal kernel; MATLAB; KERNEL-BASED REGRESSION; CROSS-VALIDATION; MODEL SELECTION; SIMPLEX-METHOD; ROBUSTNESS;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：1 / 21

页数：21

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