Principal varying coefficient estimator for high-dimensional models
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) | 1234-1250 |
| Journal / Publication | Statistics |
| Volume | 53 |
| Issue number | 6 |
| Online published | 16 Sept 2019 |
| Publication status | Published - 2019 |
Link(s)
Abstract
We consider principal varying coefficient models in the high-dimensional setting, combined with variable selection, to reduce the effective number of parameters in semiparametric modelling. The estimation is based on B-splines approach. For the unpenalized estimator, we establish non-asymptotic bounds of the estimator and then establish the (asymptotic) local oracle property of the penalized estimator, as well as non-asymptotic error bounds. Monte Carlo studies reveal the favourable performance of the estimator and an application on a real dataset is presented.
Research Area(s)
- Asymptotic properties, B-splines, sub-Gaussian distribution, ultra-high dimensionality
Citation Format(s)
Principal varying coefficient estimator for high-dimensional models. / Zhao, Weihua; Zhang, Fode; Wang, Xuejun et al.
In: Statistics, Vol. 53, No. 6, 2019, p. 1234-1250.
In: Statistics, Vol. 53, No. 6, 2019, p. 1234-1250.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
