Building mixture trees from binary sequence data

• 成果类型: Article
• 署名作者: Chen, Shu-Chuan; Lindsay, Bruce G.
• 署名单位: Arizona State University; Arizona State University-Tempe; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/93.4.843
• 发表日期: 2006
• 页码: 843860
• 关键词: mixing distribution; phylogenetic trees; maximum-likelihood; inference

摘要

We develop a new method for building a hierarchical tree from binary sequence data. It is based on an ancestral mixture model. The sieve parameter in the model plays the role of time in the evolutionary tree of the sequences. By varying the sieve parameter, one can create a hierarchical tree that estimates the population structure at each fixed backward point in time. Application to the clustering of the mitochondrial DNA sequences of Griffiths & Tavare (1994) shows that the approach performs well. Theoretical and computational properties of the ancestral mixture model are further developed.