EconPapers    
Economics at your fingertips  
 

An Inverse Norm Sign Test of Location Parameter for High-Dimensional Data

Long Feng, Binghui Liu and Yanyuan Ma

Journal of Business & Economic Statistics, 2021, vol. 39, issue 3, 807-815

Abstract: We consider the one sample location testing problem for high-dimensional data, where the data dimension is potentially much larger than the sample size. We devise a novel inverse norm sign test (INST) that is consistent and has much improved power than many existing popular tests. We further construct a general class of weighted spatial sign tests which includes these existing tests, and show that INST is the optimal member within this class, in that it is consistent and is uniformly more powerful than all other members. We establish the asymptotic null distribution and local power property of the class of tests rigorously. Extensive numerical experiments demonstrate the superiority of INST in terms of both efficiency and robustness.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/07350015.2020.1736084 (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:jnlbes:v:39:y:2021:i:3:p:807-815

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20

DOI: 10.1080/07350015.2020.1736084

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan

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

 
Page updated 2025-03-20
Handle: RePEc:taf:jnlbes:v:39:y:2021:i:3:p:807-815