Penalized likelihood phylogenetic inference: bridging the parsimony-likelihood gap.

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  • Author(s): Kim J;Kim J; Sanderson MJ
  • Source:
    Systematic biology [Syst Biol] 2008 Oct; Vol. 57 (5), pp. 665-74.
  • Publication Type:
    Journal Article; Research Support, U.S. Gov't, Non-P.H.S.
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Oxford University Press Country of Publication: England NLM ID: 9302532 Publication Model: Print Cited Medium: Internet ISSN: 1076-836X (Electronic) Linking ISSN: 10635157 NLM ISO Abbreviation: Syst Biol Subsets: MEDLINE
    • Publication Information:
      Publication: 2009- : Oxford : Oxford University Press
      Original Publication: Washington, D.C., USA : Society of Systematic Biologists, [1992-
    • Subject Terms:
      Likelihood Functions* ; Phylogeny*; Animals ; Bayes Theorem ; Drosophila/genetics ; Evolution, Molecular ; Genetic Variation ; Models, Genetic
    • Abstract:
      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.
    • Publication Date:
      Date Created: 20081015 Date Completed: 20081217 Latest Revision: 20081014
    • Publication Date:
      20240829
    • Accession Number:
      10.1080/10635150802422274
    • Accession Number:
      18853355