Shimodaira Hidetoshi
Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minatoku, Tokyo 106-8569, Japan.
Syst Biol. 2002 Jun;51(3):492-508. doi: 10.1080/10635150290069913.
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.
为减少检验偏差，针对区域的一般假设检验开发了一种使用新设计的多尺度自举技术的近似无偏（AU）检验。它被应用于最大似然树选择以获得树的置信集。AU检验基于埃弗龙等人的理论（《美国国家科学院院刊》93:13429 - 13434；1996年），但新方法提供了更高阶的精度且实现更简单。与Shimodaira - Hasegawa（SH）检验一样，AU检验调整了自举概率和Kishino - Hasegawa检验标准使用中被忽视的选择偏差。选择偏差源于同时比较许多树，并且常常导致对错误的树过度自信。SH检验虽然使用安全，但可能表现出另一种偏差，即显得保守。在这里我表明，在典型的树选择情况下，AU检验比其他方法偏差更小。这些要点在模拟研究以及哺乳动物线粒体蛋白质序列分析中得到了说明。理论论证提供了一个涵盖自举概率检验、Kishino - Hasegawa检验、AU检验和Zharkikh - Li检验的简单公式。针对在特定情况下应使用哪种检验提供了实际建议。
