Abstract #302245

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JSM 2003 Abstract #302245
Activity Number: 337
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract - #302245
Title: Clustering High-Dimensional Sequences Using Mixture Trees
Author(s): Shu-Chuan Chen*+ and Bruce G. Lindsay
Companies: Pennsylvania State University and Pennsylvania State University
Address: 326 Thomas Bldg., University Park, PA, 16802-2111,
Keywords: ancestral mixture model ; sieve parameter ; quadratic distance ; coalescent process
Abstract:

An ancestral mixture model is proposed for clustering discrete multivariate sequences. This model has a natural relationship to the coalescent process of population genetics. The sieve parameter in the model plays the role of time in the evolutionary tree of the sequences. By sliding the sieve parameter, one can create a hierarchical tree that estimates the population structure at each fixed backward point in time. Some theoretical and computational properties of the ancestral mixture model will be presented. The assessment of goodness of fit based on quadratic distance will be proposed. Furthermore, potential applications will be discussed and promising results from a case study will also be shown.

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