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February, 1995 Principal Points and Self-Consistent Points of Elliptical Distributions
Thaddeus Tarpey, Luning Li, Bernard D. Flury
Ann. Statist. 23(1): 103-112 (February, 1995). DOI: 10.1214/aos/1176324457

Abstract

The $k$ principal points of a $p$-variate random vector $\mathbf{X}$ are those points $\xi_1, \ldots, \xi_k \in \mathbb{R}^p$ which approximate the distribution of $\mathbf{X}$ by minimizing the expected squared distance of $\mathbf{X}$ from the nearest of the $\xi_j$. Any set of $k$ points $\mathbf{y}_1, \ldots, \mathbf{y}_k$ partitions $\mathbb{R}^p$ into "domains of attraction" $D_1, \ldots, D_k$ according to minimal distance; following Hastie and Stuetzle we call $\mathbf{y}_1, \ldots, \mathbf{y}_k$ self-consistent if $E\lbrack\mathbf{X}\mid\mathbf{X} \in D_j\rbrack = \mathbf{y}_j$ for $j = 1, \ldots, k$. Principal points are a special case of self-consistent points. In this paper we study principal points and self-consistent points of $p$-variate elliptical distributions. The main results are the following: (1) If $k$ self-consistent points of $\mathbf{X}$ span a subspace of dimension $q < p$, then this subspace is also spanned by $q$ principal components, that is, self-consistent points of elliptical distributions exist only in principal component subspaces. (2) The subspace spanned by $k$ principal points of $\mathbf{X}$ is identical with the subspace spanned by the principal components associated with the largest roots. This proves a conjecture of Flury. We also discuss implications of our results for the computation and estimation of principal points.

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Thaddeus Tarpey. Luning Li. Bernard D. Flury. "Principal Points and Self-Consistent Points of Elliptical Distributions." Ann. Statist. 23 (1) 103 - 112, February, 1995. https://doi.org/10.1214/aos/1176324457

Information

Published: February, 1995
First available in Project Euclid: 11 April 2007

zbMATH: 0822.62042
MathSciNet: MR1331658
Digital Object Identifier: 10.1214/aos/1176324457

Subjects:
Primary: 62H30
Secondary: 62H05, 62H25

Rights: Copyright © 1995 Institute of Mathematical Statistics

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Vol.23 • No. 1 • February, 1995
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