Optimal prediction for high-dimensional functional quantile regression in reproducing kernel Hilbert spaces
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Article number | 101568 |
| Journal / Publication | Journal of Complexity |
| Volume | 66 |
| Online published | 3 Apr 2021 |
| Publication status | Published - Oct 2021 |
Link(s)
Abstract
Regression problems with multiple functional predictors have been studied previously. In this paper, we investigate functional quantile linear regression with multiple functional predictors within the framework of reproducing kernel Hilbert spaces. The estimation procedure is based on an ℓ1-mixed-norm penalty. The learning rate of the estimator in prediction loss is established and a lower bound on the learning rate is also presented that matches the upper bound up to a logarithmic term.
Research Area(s)
- Functional data, Minimax rate, Quantile regression, Reproducing kernel Hilbert space
Citation Format(s)
Optimal prediction for high-dimensional functional quantile regression in reproducing kernel Hilbert spaces. / Yang, Guangren; Liu, Xiaohui; Lian, Heng.
In: Journal of Complexity, Vol. 66, 101568, 10.2021.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
