Skip to search boxSkip to navigationSkip to main content

Using observed confidence levels to perform principal component analyses

*Corresponding author for this work
Scholary Output:
Contribution to journal
Article
Peer-review

Abstract

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.

Publication Information

Output type

Scholary Output:
Contribution to journal
Article
Peer-review

Original language

English (US)

Pages from-to (Number of pages)

Pages 3596-3611 (16 pages)

Journal (Volume, Issue Number)

Communications in Statistics - Theory and Methods (Volume 45, Issue 12)

Publication milestones

  • Published - 06/17/2016

Publication status

Published - 06/17/2016

ISSN

0361-0926

Publication IDs

  • Scopus: 84969972548

Publication metrics

Metrics

Scopus
citations
Fractional count
1
Fractional count
0.50
Fractional count
1
Fractional count
0.50
Fractional count
1
Fractional count
1
SciVal
Author count
2
SciVal
Paper percentile
23

PlumX, opens in new tab

Captures
1
Citation count
1