# 编者信息 熊荣川 明湖实验室 xiongrongchuan@126.com http://blog.sciencenet.cn/u/Bearjazz An approximately unbiased (AU) test that uses a newly devised multiscale bootstrap technique was developed for general hypothesis testing of regions in an attempt to reduce test bias. It was applied to maximum-likelihood tree selection for obtaining the confidence set of trees. The AU test is based on the theory of Efron et al. (Proc. Natl. Acad. Sci. USA 93:13429-13434; 1996), but the new method provides higher-order accuracy yet simpler implementation. The AU test, like the Shimodaira-Hasegawa (SH) test, adjusts the selection bias overlooked in the standard use of the bootstrap probability and Kishino-Hasegawa tests. The selection bias comes from comparing many trees at the same time and often leads to overconfidence in the wrong trees. The SH test, though safe to use, may exhibit another type of bias such that it appears conservative. Here I show that the AU test is less biased than other methods in typical cases of tree selection. These points are illustrated in a simulation study as well as in the analysis of mammalian mitochondrial protein sequences. The theoretical argument provides a simple formula that covers the bootstrap probability test, the Kishino-Hasegawa test, the AU test, and the Zharkikh-Li test. A practical suggestion is provided as to which test should be used under particular circumstances. 为了减少 多区域通用假设检验偏差 ，近无偏检验（ AU test ）这一多尺度自举检验技术被开发了出来。它应用于最大似然树选择，以得到树的置信集。 AU 检验基于 Efron 等人的理论（ Proc. Natl. Acad. Sci. USA 93:13429-13434; 1996 ），但新方法精度更高，操作更简便。 AU 检验，像 Shimodaira-Hasegawa （ SH ）检验一样，调整了选择偏差，而这些偏差是被标准自举检验概率方法和 Kishino-Hasegawa 检验所忽略的。选择偏差来自于同时比较多棵树，并且常常导致错误树的过度自信。虽然使用 SH 检验较为保险，但它可能会显示出另一种类型的偏差，即偏保守。在这里，我证明了在典型的树选择情况下， AU 检验比其他方法的偏差更小。这些观点在模拟研究和哺乳动物线粒体蛋白序列分析中得到了说明。理论论证提供了一个简单的公式，涵盖了自举概率检验、 Kishino-Hasegawa 检验、 AU 检验和 Zharkikh-Li 检验。本研究还提出了在特殊情况下应采用何种检验的实用建议。 Shimodaira H . An Approximately Unbiased Test of Phylogenetic Tree Selection . Systematic Biology, 2002, 51(3):492-508.
# 编者信息 熊荣川 明湖实验室 xiongrongchuan@126.com http://blog.sciencenet.cn/u/Bearjazz Likelihood-based statistical tests of competing evolutionary hypotheses (tree topologies) have been available for approximately a decade. By far the most commonly used is the Kishino-Hasegawa test. However, the assumptions that have to be made to ensure the validity of the Kishino-Hasegawa test place important restrictions on its applicability. In particular, it is only valid when the topologies being compared are specified a priori. Unfortunately, this means that the Kishino-Hasegawa test may be severely biased in many cases in which it is now commonly used: for example, in any case in which one of the competing topologies has been selected for testing because it is the maximum likelihood topology for the data set at hand. We review the theory of the Kishino-Hasegawa test and contend that for the majority of popular applications this test should not be used. Previously published results from invalid applications of the Kishino-Hasegawa test should be treated extremely cautiously, and future applications should use appropriate alternative tests instead. We review such alternative tests, both nonparametric and parametric, and give two examples which illustrate the importance of our contentions. 基于最大似然法检验不同的进化假说（树的拓扑结构）已问世十多年。到目前为止，最常用的是 Kishino-Hasegawa 检验。然而，为确保检验有效性而必须作出的假设使其适用性受到诸多重大限制。特别是，只有当备择拓扑结构被预先指定时，它才有效。不幸的是，这意味着 Kishino-Hasegawa 检验在许多现在普遍使用的情况下可能会有严重的偏差：例如，在任何情况下，都会选择一个候选拓扑结构作为标的进行检验，因为它是现有数据集的最大似然树。我们回顾了 Kishino-Hasegawa 检验的理论，并认为对于大多数通常的应用，不应使用此检验。先前公布的 Kishino-Hasegawa 检验无效应用结果应谨慎对待，未来的应用应使用适当的替代检验。我们回顾了非参数检验和参数检验，并给出了两个例子来说明本争论的重要性。 Goldman, Anderson J P , Allen G , et al. Likelihood-Based Tests of Topologies in Phylogenetics . Systematic Biology, 2000, 49(4):652-670.
标签: 最大似然
