Kim Junhyong, Sanderson Michael J
Department of Biology and Penn Genome Frontiers Institute, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Syst Biol. 2008 Oct;57(5):665-74. doi: 10.1080/10635150802422274.
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.
用于系统发育估计的分子数据日益增加的多样性和异质性，促使了复杂模型和基于模型的估计器的发展。在此，我们提出一种惩罚似然（PL）框架，其中基础模型的复杂程度可以得到平滑控制。我们展示了针对四分类单元树情形的PL框架，并研究其性质。PL框架产生一种估计器，目前使用的大多数估计器，如最大简约估计器、齐次似然估计器、伽马混合似然估计器等，都成为单一PL估计器族的特殊情况。此外，使用适当的惩罚函数，基础模型的复杂性可以被划分为单独控制的类别，从而灵活控制模型复杂性。
