EconPapers    
Economics at your fingertips  
 

Test for bandedness of high-dimensional precision matrices

Guanghui Cheng, Zhengjun Zhang and Baoxue Zhang

Journal of Nonparametric Statistics, 2017, vol. 29, issue 4, 884-902

Abstract: Statistical inferences in high-dimensional precision matrices are equally important as statistical inferences in high-dimensional covariance matrices. In the literature, much attention has been paid to the latter, and significant advances have been achieved, especially in estimation and test of the banded structure. This paper proposes a new test for testing banded structures of precision matrices without assuming any specific parametric distribution. The test is adapted to the large p small n problems in which we derive the asymptotic distribution under the null hypothesis of bandedness. Simulation results show that the proposed test performs well with finite sample sizes. A real data application is realised to a phone call centre data.

Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link)
http://hdl.handle.net/10.1080/10485252.2017.1375112 (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:taf:gnstxx:v:29:y:2017:i:4:p:884-902

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20

Access Statistics for this article

Journal of Nonparametric Statistics is currently edited by Jun Shao

More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2018-08-04
Handle: RePEc:taf:gnstxx:v:29:y:2017:i:4:p:884-902