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Consistency of kernel-based quantile regression
| Content Provider | Semantic Scholar |
|---|---|
| Author | Christmann, Andreas Steinwart, Ingo |
| Copyright Year | 2006 |
| Abstract | Quantile regression is used in many areas of applied research and business. Examples are actuarial, financial or biometrical applications. We show that a non-parametric generalization of quantile regression based on kernels shares with support vector machines the property of consistency to the Bayes risk. We further use this consistency to prove that the non-parametric generalization approximates the conditional quantile function which gives the mathematical justification for kernel-based quantile regression. Copyright q 2008 John Wiley & Sons, Ltd. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.stoch.uni-bayreuth.de/de/CHRISTMANN/Christmann_files/ChristmannSteinwart2008ApplStoch.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Copyright Generalization (Psychology) John D. Wiley Kernel (operating system) Mathematics Quantile Support Vector Machine applied research |
| Content Type | Text |
| Resource Type | Article |
