Testing for the redundancy of variables in principal components analysis

James R. Schott

Statistics & Probability Letters, 1991, vol. 11, issue 6, 495-501

Abstract: When all of the important principal components have zero coefficients on the same original variables, then those variables are redundant and may be eliminated. Tyler (1981) derived a statistic suitable for testing such a hypothesis. An asymptotic expansion for the mean of this statistic is obtained and used to calculate a Bartlett adjustment factor. The performances of the unadjusted and adjusted statistics are investigated in a simulation.

Keywords: Asymptotic; expansion; Bartlett; adjustment; factor; dimensionality; reduction (search for similar items in EconPapers)
Date: 1991
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0167-7152(91)90114-7
Full text for ScienceDirect subscribers only

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:eee:stapro:v:11:y:1991:i:6:p:495-501

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().