Accession Number:

AD0601468

Title:

OPTIMUM CLASSIFICATION RULES FOR CLASSIFICATION INTO TWO MULTIVARIATE NORMAL POPULATIONS.

Descriptive Note:

Corporate Author:

COLUMBIA UNIV NEW YORK

Personal Author(s):

• Gupta,S. Das

Report Date:

1964-03-26

Pagination or Media Count:

28.0

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.

Descriptors:

• *POPULATION(MATHEMATICS)
• *STATISTICAL ANALYSIS
• STATISTICAL DISTRIBUTIONS
• POPULATION(MATHEMATICS)
• MATRICES(MATHEMATICS)
• PROBABILITY
• STATISTICAL FUNCTIONS
• VECTOR ANALYSIS
• TRANSFORMATIONS(MATHEMATICS)

Subject Categories:

Distribution Statement:

APPROVED FOR PUBLIC RELEASE