BAYESIAN ESTIMATION OF LATENT ROOTS AND VECTORS, WITH SPECIAL REFERENCE TO THE BIVARIATE NORMAL DISTRIBUTION.

Abstract

The paper discusses some aspects of the estimation of latent roots and vectors of the covariance matrix of the bivariate normal distribution from a Bayesian viewpoint. The joint distribution of (1) the angle of the canonical transformation and (2) the ratio of the larger root to the total variance is considered in detail and illustrated by an example. Also discussed is the problem of making inferences about the larger roots, and several simple approximations to its distribution are considered. Finally a generalization of one of the approximation methods to latent roots of higher dimensional convariance matrix is given. (Author)

Document Details

Document Type
Technical Report
Publication Date
May 01, 1968
Accession Number
AD0679203

Entities

People

  • George C. Tiao
  • Stephen E. Fienberg

Organizations

  • University of Wisconsin–Madison

Tags

DTIC Thesaurus Topics

  • Computing-Related Activities
  • Covariance
  • Data Science
  • Distribution Functions
  • Functions (Mathematics)
  • Information Science
  • Interdisciplinary Science
  • Mathematical Analysis
  • Mathematics
  • Normal Distribution

Fields of Study

  • Mathematics

Readers

  • Linear Algebra
  • Statistical inference.

Technology Areas

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