Accession Number : ADA288295

Title :   A Method to Determine the Structure of an Unknown Mixture Using the Akaike Information Criterion and the Bootstrap.

Descriptive Note : Technical rept.,

Corporate Author : GEORGE MASON UNIV FAIRFAX VA CENTER FOR COMPUTATIONAL STATISTICS

Personal Author(s) : Solka, Jeffrey L. ; Wegman, Edward J. ; Priebe, Carey E. ; Poston, Wendy L. ; Rogers, George W.

PDF Url : ADA288295

Report Date : OCT 1994

Pagination or Media Count : 30

Abstract : Given i.i.d. observations x1,x2,x3,,,,xn drawn from a mixture of normal terms one is often interested in determining the number of terms in the mixture and their defining parameters. Although the problem of determining the number of terms is intractable under the most general assumptions there is hope of elucidating the mixture structure given appropriate caveats on the underlying mixture. This paper examines a new approach to this problem based on the use of Akaike Information Criterion (AIC) based pruning of data driven mixture models which are obtained from resampled data sets. Results of the application of this procedure to artificially generated and real world data sets are provided.

Descriptors :   *PROBABILITY DENSITY FUNCTIONS, *NONPARAMETRIC STATISTICS, DATA BASES, MATHEMATICAL MODELS, STOCHASTIC PROCESSES, DATA MANAGEMENT, PARAMETERS, MAXIMUM LIKELIHOOD ESTIMATION, MULTIVARIATE ANALYSIS, PROBABILITY DISTRIBUTION FUNCTIONS, STATISTICAL DATA, DEGREES OF FREEDOM.

Subject Categories : Statistics and Probability
      Operations Research

Distribution Statement : APPROVED FOR PUBLIC RELEASE