OPTIMUM CLASSIFICATION RULES FOR CLASSIFICATION INTO TWO MULTIVARIATE NORMAL POPULATIONS.

Abstract

It is shown that the maximum likelihood classification rule is unbiased, admissible and minimax when the common covariance matrix of the two normal propulations is known and when the common covariance matrix is unknown, the corresponding maximum likelihood rule is unbiased and in an invariant class it is also minimax and admissible. The loss function in each problem is assumed to be a function (satisfying some mild restrictions) of the Mahalanobis distance between the two populations.

Document Details

Document Type
Technical Report
Publication Date
Mar 26, 1964
Accession Number
AD0601468

Entities

People

  • S. Das Gupta

Organizations

  • Columbia University

Tags

DTIC Thesaurus Topics

  • Classification
  • Cooperation
  • Covariance
  • Data Science
  • Information Science
  • North Carolina

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

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