Using observed confidence levels to perform principal component analyses

Alan M. Polansky and Santu Ghosh

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 12, 3596-3611

Abstract: This paper focuses on applying the method of observed confidence levels to problems commonly encountered in principal component analyses. In particular, we focus on assigning levels of confidence to the number of components that explain a specified proportion of variation in the original data. Approaches based on the normal model as well as a non parametric model are explored. The usefulness of the methods are discussed using an example and an empirical study.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2014.904356 (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:45:y:2016:i:12:p:3596-3611

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

DOI: 10.1080/03610926.2014.904356

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 ().