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A Fast Distance Based Approach for Determining the Number
of Components in Mixtures

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Sujit K. Sahu and Russell Cheng
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SUMMARY

The problem of determining the unknown number of components in mixtures is
of considerable interest to researchers in many areas. This paper
generalizes a Bayesian testing method based on the Kullback-Leibler distance
proposed by Mengersen and Robert (1996). An alternative, weighted
Kullback-Leibler distance is proposed as testing criterion. Explicit
formulas for this distance are given for a number of mixture distributions.
A step-wise testing procedure is proposed to select the minimum number of
components adequate for the data. A fast, collapsing approach is proposed
for reducing the number of components which does not require full refitting
at each step. The method, using both distances, is compared to the Bayes
factor approach. The method is easy to implement and is illustrated using
\texttt{BUGS} (a general purpose software for Bayesian analysis).

The paper in postscript
or in pdf format.

### BUGS files only in odc format, you need to save it first.

normal mixture
gamma mixture

Back to my page.
S.K.Sahu@maths.soton.ac.uk