Zharkikh A, Li W H
Center for Demographic and Population Genetics, University of Texas, Houston 77225, USA.
Mol Phylogenet Evol. 1995 Mar;4(1):44-63. doi: 10.1006/mpev.1995.1005.
The bootstrap is a statistical technique that is widely used to assess confidence limits on phylogenies. We show that the power of the bootstrap test is lower than those of the C and S tests suggested by Felsenstein, unless the critical value employed in the bootstrap test is correctly selected. If the 95% critical value is used, the bootstrap proportions are underestimates of the confidence level when the number of possible alternative topologies is three or more; the degree of underestimation increases with the number of competing alternative topologies. To overcome this problem, we propose the complete-and-partial bootstrap technique as a method for obtaining an unbiased estimate of the confidence level. The method is based on a multinomial model of many alternatives among which the choice is to be made. The complete-and-partial bootstrap technique can be used to estimate the effective number of competing alternative topologies and the confidence level of the monophyly of a particular group of taxa or of an inferred tree topology. This approach can be used with the maximum parsimony or neighbor-joining tree reconstruction method.
自展是一种广泛用于评估系统发育树置信限的统计技术。我们表明，自展检验的效力低于费尔斯滕森提出的C检验和S检验，除非自展检验中使用的临界值被正确选择。如果使用95%的临界值，当可能的替代拓扑结构数量为三个或更多时，自展比例会低估置信水平；低估程度会随着竞争替代拓扑结构的数量增加而增加。为克服此问题，我们提出完全和部分自展技术作为获得置信水平无偏估计的一种方法。该方法基于众多替代方案中的多项模型，从中进行选择。完全和部分自展技术可用于估计竞争替代拓扑结构的有效数量以及特定分类单元组或推断树拓扑结构单系性的置信水平。这种方法可与最大简约法或邻接法树重建方法一起使用。
