Exact sampling distribution of the general case sample correlation matrix

Nicy Sebastian and T. Princy

Communications in Statistics - Theory and Methods, 2024, vol. 53, issue 18, 6380-6393

Abstract: The distribution of the sample correlation matrix when the sample comes from a real multivariate normal population and when the population covariance matrix is diagonal or when the population variables are independently distributed is available in the literature. In the general case, that is, when the covariance matrix in the normal population is a general real positive definite matrix, the distribution of the sample correlation matrix is not available in a compact computable form in the literature. Some scattered results on some aspects of the general case are available, but the representations are not easily tractable. This article deals with a covariance structure coming from a real matrix-variate gamma when the scale parameter matrix is a general real positive definite matrix. General procedures for deriving asymptotic chi-square and asymptotic normal for some general moment structures are also presented in this article. The density in some particular cases are given explicitly. Graphs are also provided in the particular cases.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2023.2244622 (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:53:y:2024:i:18:p:6380-6393

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

DOI: 10.1080/03610926.2023.2244622

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 ().