Accession Number:

ADA112469

Title:

Multi-Sample Cluster Analysis Using Akaike's Information Criterion.

Descriptive Note:

Technical rept.

Corporate Author:

ILLINOIS UNIV AT CHICAGO CIRCLE DEPT OF QUANTITATIVE METHODS

Report Date:

1982-01-30

Pagination or Media Count:

38.0

Abstract:

Multi-sample cluster analysis, the problem of grouping samples, is studied from an information-theoretic viewpoint via Akaikes Information Criterion AIC. This criterion combines the maximum value of the likelihood with the number of parameters used in achieving that value. The multi-sample cluster problem is defined, and AIC is developed for this problem. The form of AIC is derived in both univariate and multivariate analysis of variance models. Numerical examples are presented and results are shown to demonstrate the utility of AIC in identifying the best clustering alternatives.

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE