Nonconvex penalized reduced rank regression and its oracle properties in high dimensions

Heng Lian and Yongdai Kim

Journal of Multivariate Analysis, 2016, vol. 143, issue C, 383-393

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.

Keywords: Model selection; Nonconvex penalty; Oracle property; Reduced rank regression (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047259X15002493
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:143:y:2016:i:c:p:383-393

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.jmva.2015.09.023

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().