Nonconvex penalized reduced rank regression and its oracle properties in high dimensions
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 383-393 |
Journal / Publication | Journal of Multivariate Analysis |
Volume | 143 |
Publication status | Published - 1 Jan 2016 |
Externally published | Yes |
Link(s)
Abstract
Sparse reduced rank regression achieves dimension reduction and variable selection simultaneously. In this paper, for a class of nonconvex penalties, we give sufficient conditions that guarantee the oracle estimator is a local minimizer and stronger conditions that guarantee it is a global minimizer, with probability tending to one in an ultra-high dimensional setting. We carry out simulations to investigate the performance of the estimator. A real data set is analyzed for illustration.
Research Area(s)
- Model selection, Nonconvex penalty, Oracle property, Reduced rank regression
Citation Format(s)
Nonconvex penalized reduced rank regression and its oracle properties in high dimensions. / Lian, Heng; Kim, Yongdai.
In: Journal of Multivariate Analysis, Vol. 143, 01.01.2016, p. 383-393.
In: Journal of Multivariate Analysis, Vol. 143, 01.01.2016, p. 383-393.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review