Projects per year
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.
Original language | English |
---|---|
Article number | 101568 |
Journal | Journal of Complexity |
Volume | 66 |
Online published | 3 Apr 2021 |
DOIs | |
Publication status | Published - Oct 2021 |
Research Keywords
- Functional data
- Minimax rate
- Quantile regression
- Reproducing kernel Hilbert space
Fingerprint
Dive into the research topics of 'Optimal prediction for high-dimensional functional quantile regression in reproducing kernel Hilbert spaces'. Together they form a unique fingerprint.-
GRF: Distributed Estimation with Random Projection in Reproducing Kernel Hilbert Spaces
LIAN, H. (Principal Investigator / Project Coordinator)
1/01/22 → …
Project: Research
-
GRF: Low-rank tensor as a Dimension Reduction Tool in Complex Data Analysis
LIAN, H. (Principal Investigator / Project Coordinator)
1/01/20 → 28/11/24
Project: Research
-
GRF: Divide and Conquer in High-dimensional Statistical Models
LIAN, H. (Principal Investigator / Project Coordinator)
1/10/18 → 24/08/23
Project: Research