在缺失数据的随机模型下，NJst和ASTRID在统计学上不一致
NJst and ASTRID are not statistically consistent under a random model of missing data
摘要：
由于多种原因，从多位点数据集进行物种树估计在统计上具有挑战性，包括由于不完全谱系排序（ILS）导致的基因组中的基因树异质性。已经开发了物种树估计方法，其通过估计基因树并且然后使用那些基因树来估计物种树来操作。这些方法中的几种（例如，ASTRAL、ASTRID和NJst）在多物种聚结（MSC）模型下可证明是统计上一致的，前提是基因树被正确估计，并且没有缺失数据。最近，Nute等人（BMC Genomics 2018）解决了这些方法在分类单元缺失的随机模型下是否保持统计一致的问题，并断言它们确实如此。在这里，我们提供了一个反例，这些定理之一，并建立ASTRID和NJst是不统计一致下的独立同分布。分类单元删除模型。
英文摘要：
Species tree estimation from multi-locus datasets is statistically challenging for multiple reasons, including gene tree heterogeneity across the genome due to incomplete lineage sorting (ILS). Species tree estimation methods have been developed that operate by estimating gene trees and then using those gene trees to estimate the species tree. Several of these methods (e.g., ASTRAL, ASTRID, and NJst) are provably statistically consistent under the multi-species coalescent (MSC) model, provided that the gene trees are estimated correctly, and there is no missing data. Recently, Nute et al. (BMC Genomics 2018) addressed the question of whether these methods remain statistically consistent under random models of taxon deletion, and asserted that they do so. Here we provide a counterexample to one of these theorems, and establish that ASTRID and NJst are not statistically consistent under an i.i.d. model of taxon deletion.
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