Robust Principal Components and Dispersion Matrices via Projection Pursuit.

Abstract

This paper discusses a new kind of robust procedure for estimating covariance/correlation matrices and their principal components. Robust eigenvectors and eigenvalues of a covariance matrix are obtained by the projection pursuit method (PP) with robust variance as a projection index. Monte Carlo simulation results show that the best of the three projection pursuit type procedures introduced in this study compares favorably with approaches based on M-estimators of covariance: the estimate obtained by the new procedure has about the same bias and variance as the best M-estimators, and a somewhat better breakdown point. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1981
Accession Number
ADA107827

Entities

People

  • Guoying Li
  • Zhonglian Chen

Organizations

  • Harvard University

Tags

DTIC Thesaurus Topics

  • Covariance
  • Data Science
  • Dispersions
  • Eigenvalues
  • Eigenvectors
  • Estimators
  • Information Science
  • Monte Carlo Method
  • Plastic Explosives
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Linear Algebra
  • Statistical inference.