A semiparametric model for matrix regression
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Article number | 2250001 |
| Journal / Publication | Random Matrices: Theory and Application |
| Volume | 10 |
| Issue number | 2 |
| Online published | 12 Aug 2020 |
| Publication status | Published - Apr 2021 |
Link(s)
Abstract
We focus on regression problems in which the predictors are naturally in the form of matrices. Reduced rank regression and related regularized method have been adapted to matrix regression. However, linear methods are restrictive in their expressive power. In this work, we consider a class of semiparametric additive models based on series estimation of nonlinear functions which interestingly induces a problem of 3rd order tensor regression with transformed predictors. Risk bounds for the estimator are derived and some simulation results are presented to illustrate the performances of the proposed method.
Research Area(s)
- Additive models, matrix regression, tensor regression, Tucker decomposition
Citation Format(s)
A semiparametric model for matrix regression. / Zhao, Weihua; Zhang, Xiaoyu; Lian, Heng.
In: Random Matrices: Theory and Application, Vol. 10, No. 2, 2250001, 04.2021.
In: Random Matrices: Theory and Application, Vol. 10, No. 2, 2250001, 04.2021.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
