Penalized likelihood phylogenetic inference: Bridging the parsimony-likelihood gap

Junhyong Kim, Michael J. Sanderson

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)665-674
Number of pages10
JournalSystematic biology
Issue number5
StatePublished - Oct 2008


  • Model selection
  • Penalized likelihood
  • Phylogeny estimation
  • Semi-parametric

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics


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