Closed Likelihood Ratio Testing Procedures to Assess Similarity of Covariance Matrices

Francesca Greselin () and Antonio Punzo

The American Statistician, 2013, vol. 67, issue 3, 117-128

Abstract: In this article, we introduce a multiple testing procedure to assess a common covariance structure between k groups. The new test allows for a choice among eight different patterns arising from the three-term eigen decomposition of the group covariances. It is based on the closed testing principle and adopts local likelihood ratio (LR) tests. The approach reveals richer information about the underlying data structure than classical methods, the most common one being only based on homo/heteroscedasticity. At the same time, it provides a more parsimonious parameterization, whenever the constrained model is suitable to describe the real data. The new inferential methodology is then applied to some well-known datasets chosen from the multivariate literature. Finally, simulation results are presented to investigate its performance in different situations representing gradual departures from homoscedasticity and to evaluate the reliability of using the asymptotic χ-super-2 to approximate the actual distribution of the local LR test statistics.

Date: 2013
References: View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://hdl.handle.net/10.1080/00031305.2013.791643 (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:amstat:v:67:y:2013:i:3:p:117-128

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UTAS20

DOI: 10.1080/00031305.2013.791643

Access Statistics for this article

The American Statistician is currently edited by Eric Sampson

More articles in The American Statistician from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().