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A Bias in ML Estimates of Branch Lengths in the Presence of Multiple Signals

Penny, David; White, W.T.; Hendy, Mike D.; Phillips, Matthew


Sequence data often have competing signals that are detected by network programs or Lento plots. Such data can be formed by generating sequences on more than one tree, and combining the results, a mixture model. We report that with such mixture models, the estimates of edge (branch) lengths from maximum likelihood (ML) methods that assume a single tree are biased. Based on the observed number of competing signals in real data, such a bias of ML is expected to occur frequently. Because network...[Show more]

CollectionsANU Research Publications
Date published: 2008
Type: Journal article
Source: Molecular Biology and Evolution
DOI: 10.1093/molbev/msm263


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