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

Penny, D., White, W.T., Hendy, M.D., & Phillips, M.J. (2008) A Bias in ML Estimates of Branch Lengths in the Presence of Multiple Signals. Molecular Biology and Evolution, 25(2), pp. 239-242.

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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 methods can recover competing signals more accurately, there is a need for ML methods allowing a network. A fundamental problem is that mixture models can have more parameters than can be recovered from the data, so that some mixtures are not, in principle, identifiable. We recommend that network programs be incorporated into best practice analysis, along with ML and Bayesian trees.

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13 citations in Web of Science®

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ID Code: 50536
Item Type: Journal Article
Refereed: Yes
Additional Information: Articles free to read on journal website after 12 months
Keywords: Maximum likehood estimation, Mixture models, Multiple signals
DOI: 10.1093/molbev/msm263
ISSN: 1537-1719
Subjects: Australian and New Zealand Standard Research Classification > BIOLOGICAL SCIENCES (060000) > GENETICS (060400)
Divisions: Current > Schools > School of Earth, Environmental & Biological Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2008 Oxford University Press
Deposited On: 24 May 2012 05:01
Last Modified: 05 Feb 2015 05:39

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