Principal points for an allometric extension model

Shun Matsuura, Hiroshi Kurata

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

A set of n-principal points of a p-dimensional distribution is an optimal n-point-approximation of the distribution in terms of a squared error loss. It is in general difficult to derive an explicit expression of principal points. Hence, we may have to search the whole space Rp for n-principal points. Many efforts have been devoted to establish results that specify a linear subspace in which principal points lie. However, the previous studies focused on elliptically symmetric distributions and location mixtures of spherically symmetric distributions, which may not be suitable to many practical situations. In this paper, we deal with a mixture of elliptically symmetric distributions that form an allometric extension model, which has been widely used in the context of principal component analysis. We give conditions under which principal points lie in the linear subspace spanned by the first several principal components.

Original languageEnglish
Pages (from-to)853-870
Number of pages18
JournalStatistical Papers
Volume55
Issue number3
DOIs
Publication statusPublished - 2014

Fingerprint

Principal Points
Elliptically Symmetric Distributions
Spherically Symmetric Distribution
Subspace
Squared Error Loss
Model
Principal Components
Principal Component Analysis
Approximation

Keywords

  • Allometric extension model
  • Elliptically symmetric distribution
  • Multivariate mixture distribution
  • Principal components
  • Principal points
  • Principal subspace theorem

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Principal points for an allometric extension model. / Matsuura, Shun; Kurata, Hiroshi.

In: Statistical Papers, Vol. 55, No. 3, 2014, p. 853-870.

Research output: Contribution to journalArticle

Matsuura, Shun ; Kurata, Hiroshi. / Principal points for an allometric extension model. In: Statistical Papers. 2014 ; Vol. 55, No. 3. pp. 853-870.
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