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• W2131744420 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." @default.
• W2131744420 created "2016-06-24" @default.
• W2131744420 creator A5030624705 @default.
• W2131744420 creator A5085644660 @default.
• W2131744420 date "2008-10-01" @default.
• W2131744420 modified "2023-09-27" @default.
• W2131744420 title "Penalized Likelihood Phylogenetic Inference: Bridging the Parsimony-Likelihood Gap" @default.
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• W2131744420 doi "https://doi.org/10.1080/10635150802422274" @default.
• W2131744420 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/18853355" @default.
• W2131744420 hasPublicationYear "2008" @default.
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