期刊
SYSTEMATIC BIOLOGY
卷 57, 期 5, 页码 665-674出版社
OXFORD UNIV PRESS
DOI: 10.1080/10635150802422274
关键词
Model selection; penalized likelihood; phylogeny estimation; semi-parametric
资金
- NSF [DEB 0334866, DEB 0715370, DEB 0733365]
The increasing diversity and heterogeneity of molecular data for phylogeny estimation has led to development of complex models and model-based estimators. Here, we propose a penalized likelihood (PL) framework in which the levels of complexity in the underlying model can be smoothly controlled. We demonstrate the PL framework for a four-taxon tree case and investigate its properties. The PL framework yields an estimator in which the majority of currently employed estimators such as the maximum-parsimony estimator, homogeneous likelihood estimator, gamma mixture likelihood estimator, etc., become special cases of a single family of PL estimators. Furthermore, using the appropriate penalty function, the complexity of the underlying models can be partitioned into separately controlled classes allowing flexible control of model complexity.
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