The Annals of Statistics

A Class of Asymptotic Tests for Principal Component Vectors

David E. Tyler

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Abstract

In this paper, the hypothesis that a set of vectors lie in the subspace spanned by a prescribed subset of the principal component vectors for a normal population is considered. A class of invariant asymptotic tests based on the sample covariance matrix is derived. Tests in this class are shown to be consistent and their local power functions are given. The arguments used in deriving the class of tests are not heavily dependent on the assumption of normality nor on the use of the sample covariance matrix. The results are shown to generalize when the procedures are based on any affine-invariant $M$-estimate of scatter and when the population is elliptical.

Article information

Source
Ann. Statist., Volume 11, Number 4 (1983), 1243-1250.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176346337

Digital Object Identifier
doi:10.1214/aos/1176346337

Mathematical Reviews number (MathSciNet)
MR720269

Zentralblatt MATH identifier
0544.62053

JSTOR
links.jstor.org

Subjects
Primary: 62H15: Hypothesis testing
Secondary: 62H20: Measures of association (correlation, canonical correlation, etc.) 62H25: Factor analysis and principal components; correspondence analysis 62E20: Asymptotic distribution theory

Keywords
Elliptical distributions invariance noncentral Wishart robustness spectral decomposition

Citation

Tyler, David E. A Class of Asymptotic Tests for Principal Component Vectors. Ann. Statist. 11 (1983), no. 4, 1243--1250. doi:10.1214/aos/1176346337. https://projecteuclid.org/euclid.aos/1176346337


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