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.
Original language | English (US) |
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Pages (from-to) | 3596-3611 |
Number of pages | 16 |
Journal | Communications in Statistics - Theory and Methods |
Volume | 45 |
Issue number | 12 |
DOIs | |
State | Published - Jun 17 2016 |
Keywords
- Eigenvalue
- Eigenvector
- Multivariate analysis
- Non parametric bootstrap
- Parametric bootstrap
ASJC Scopus subject areas
- Statistics and Probability