A Bias in ML Estimates of Branch Lengths in the Presence of Multiple Signals
-
Altmetric Citations
Penny, David; White, W.T.; Hendy, Mike D.; Phillips, Matthew
Description
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]
Collections | ANU Research Publications |
---|---|
Date published: | 2008 |
Type: | Journal article |
URI: | http://hdl.handle.net/1885/52610 |
Source: | Molecular Biology and Evolution |
DOI: | 10.1093/molbev/msm263 |
Download
File | Description | Size | Format | Image |
---|---|---|---|---|
01_Penny_A_Bias_in_ML_Estimates_of_2008.pdf | 192.4 kB | Adobe PDF | Request a copy |
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.
Updated: 17 November 2022/ Responsible Officer: University Librarian/ Page Contact: Library Systems & Web Coordinator