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Learning rates for the risk of kernel-based quantile regression estimators in additive models

Title data

Christmann, Andreas ; Zhou, Ding-Xuan:
Learning rates for the risk of kernel-based quantile regression estimators in additive models.
In: Analysis and Applications. Vol. 14 (2016) Issue 3 . - pp. 449-477.
ISSN 0219-5305
DOI: https://doi.org/10.1142/S0219530515500050

Official URL: Volltext

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Project information

Project title:
Project's official titleProject's id
Support Vector Machines bei stochastischer Abhaengigkeit (German) Support Vector Machines under stochastic dependencyCH/291/2-1

Project financing: Deutsche Forschungsgemeinschaft
Deutsche Forschungsgesellschaft

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Additive model; quantile regression; rate of convergence; support vector machine
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics VII - Stochastics
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics VII - Stochastics > Chair Mathematics VII - Stochastics - Univ.-Prof. Dr. Andreas Christmann
Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 310 Statistics
500 Science > 510 Mathematics
Date Deposited: 28 Oct 2015 07:22
Last Modified: 13 Jun 2016 08:50
URI: https://eref.uni-bayreuth.de/id/eprint/20514