On the oracle property of adaptive group Lasso in high-dimensional linear models

Caiya Zhang () and Yanbiao Xiang

Statistical Papers, 2016, vol. 57, issue 1, 249-265

Abstract: In this paper, we consider the adaptive group Lasso in high-dimensional linear regression. Some extensions have been done with other fitting procedures, such as adaptive Lasso, nonconcave penalized likelihood and adaptive elastic-net. Under appropriate conditions, we establish the consistency and asymptotic normality, which means that the adaptive group Lasso shares the oracle property in high-dimensional linear regression when the number of group variables diverges with the sample size. Copyright Springer-Verlag Berlin Heidelberg 2016

Keywords: Adaptive group Lasso; Oracle property; High dimensionality; Regression model (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://hdl.handle.net/10.1007/s00362-015-0684-0 (text/html)
Access to full text is restricted to subscribers.

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:spr:stpapr:v:57:y:2016:i:1:p:249-265

Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362

DOI: 10.1007/s00362-015-0684-0

Access Statistics for this article

Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller

More articles in Statistical Papers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().