Asymptotic Inference for Eigenvectors.

Abstract

Asymptotic procedures are given for testing certain hypotheses concerning eigenvectors and for constructing confidence regions for eigenvectors. These asymptotic procedures are derived under fairly general conditions on the estimates of the matrix whose eigenvectors are of interest. Applications of the general results to principal components analysis and canonical variate analysis are given. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1979
Accession Number
ADA078960

Entities

People

  • David E. Tyler

Organizations

  • Princeton University

Tags

Communities of Interest

  • Advanced Electronics
  • C4I

DTIC Thesaurus Topics

  • Algebra
  • Asymptotic Normality
  • Chi Square Test
  • Covariance
  • Data Science
  • Distribution Theory
  • Eigenvalues
  • Eigenvectors
  • Hypotheses
  • Information Science
  • Linear Algebra
  • Matrix Theory
  • Multivariate Analysis
  • Normal Distribution
  • Sequences
  • Statistics
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Control Systems Engineering.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms