An optimal projection test for zero multiple correlation coefficient in high-dimensional normal data

D. Najarzadeh

Communications in Statistics - Theory and Methods, 2022, vol. 51, issue 4, 1011-1028

Abstract: Testing the hypothesis of zero multiple correlation coefficient is of interest in wide variety of applications including multiple regression analysis. In high-dimensional data, traditional testing procedures to test this hypothesis become practically infeasible due to the singularity of the sample covariance matrix. To deal with this problem, an optimal projection test with a computationally simple and efficient algorithm for implementation is proposed, which can also be used in low-dimensional data. Some simulations are performed to evaluate the performance of the proposed test in high-dimensional normal data as well as to compare the proposed test with the classical exact test in low-dimensional normal data. Lastly, the experimental validation of the proposed approach is carried out on mice tumor volumes data.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2020.1757111 (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:lstaxx:v:51:y:2022:i:4:p:1011-1028

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2020.1757111

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().