On double-index dimension reduction for partially functional data
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 |
|---|---|
| Pages (from-to) | 761-768 |
| Journal / Publication | Journal of Nonparametric Statistics |
| Volume | 31 |
| Issue number | 3 |
| Online published | 20 Jun 2019 |
| Publication status | Published - Sept 2019 |
Link(s)
Abstract
In this note, we consider the situation where we have a functional predictor as well as some more traditional scalar predictors, which we call the partially functional problem. We propose a semiparametric model based on sufficient dimension reduction, and thus our main interest is in dimension reduction although prediction can be carried out at a second stage. We establish root-n consistency of the linear part of the estimator. Some Monte Carlo studies are carried out as proof of concept.
Research Area(s)
- Functional data analysis, regularisation, sliced inverse regression
Citation Format(s)
On double-index dimension reduction for partially functional data. / Yang, Guangren; Lin, Hongmei; Lian, Heng.
In: Journal of Nonparametric Statistics, Vol. 31, No. 3, 09.2019, p. 761-768.
In: Journal of Nonparametric Statistics, Vol. 31, No. 3, 09.2019, p. 761-768.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
