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

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.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1994
Accession Number
ADA288295

Entities

People

  • Carey E. Priebe
  • Edward Wegman
  • George W. Rogers
  • Jeffrey L. Solka
  • Wendy L. Poston

Organizations

  • George Mason University

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  • Biomedical
  • Materials and Manufacturing Processes

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Fields of Study

  • Computer science

Readers

  • Statistical inference.
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