Optimal principal points estimators of multivariate distributions of location-scale and location-scale-rotation families

Shun Matsuura, Thaddeus Tarpey

Research output: Contribution to journalArticle

Abstract

A set of k points that optimally summarize a distribution is called a set of k-principal points, which is a generalization of the mean from one point to multiple points and is useful especially for multivariate distributions. This paper discusses the estimation of principal points of multivariate distributions. First, an optimal estimator of principal points is derived for multivariate distributions of location-scale families. In particular, an optimal principal points estimator of a multivariate normal distribution is shown to be obtained by using principal points of a scaled multivariate t-distribution. We also study the case of multivariate location-scale-rotation families. Numerical examples are presented to compare the optimal estimators with maximum likelihood estimators.

Original languageEnglish
Pages (from-to)1629-1643
Number of pages15
JournalStatistical Papers
Volume61
Issue number4
DOIs
Publication statusPublished - 2020 Aug 1

Keywords

  • Location-scale family
  • Location-scale-rotation family
  • Multivariate normal distribution
  • Multivariate t-distribution
  • Principal points

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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