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- Title
Asymptotic analysis of quantile regression learning based on coefficient dependent regularization.
- Authors
Li, Meng; Sun, Hong-Wei
- Abstract
In this paper, we consider conditional quantile regression learning algorithms based on the pinball loss with data dependent hypothesis space and ℓ2-regularizer. Functions in this hypothesis space are linear combination of basis functions generated by a kernel function and sample data. The only conditions imposed on the kernel function are the continuity and boundedness which are pretty weak. Our main goal is to study the consistency of this regularized quantile regression learning. By concentration inequality with ℓ2-empirical covering numbers and operator decomposition techniques, satisfied error bounds and convergence rates are explicitly derived.
- Subjects
QUANTILE regression; MACHINE learning; COEFFICIENTS (Statistics); MATHEMATICAL regularization; KERNEL functions; MATHEMATICAL inequalities
- Publication
International Journal of Wavelets, Multiresolution & Information Processing, 2015, Vol 13, Issue 4, p-1
- ISSN
0219-6913
- Publication type
Article
- DOI
10.1142/S0219691315500186