Accession Number:

ADA124432

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:

1982-12-20

Pagination or Media Count:

32.0

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 the multivariate analysis of variance MANOVA model and in the multivariate model with varying mean vectors and variance-covariance matrices. Numerical examples are presented for AIC and another criterion called w-square. The results demonstrate the utility of AIC in identifying the best clustering alternatives. Author

Subject Categories:

  • Statistics and Probability
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE