论文标题
在丢失数据的随机模型下，NJST和Astrid在统计上不一致
NJst and ASTRID are not statistically consistent under a random model of missing data
论文作者
论文摘要
由于多种原因，由于谱系分类（ILS）而导致的基因组基因树异质性（ILS），因此在多种原因上进行了多种基因组的物种树在统计上具有挑战性。已经开发了通过估计基因树，然后使用这些基因树来估计物种树的物种树估计方法。在多种物种合并（MSC）模型下，这些方法（例如，星体，Astrid和NJST）在统计学上是一致的，只要估计了基因树，并且没有丢失的数据。最近，Nute等。 （BMC Genomics 2018）解决了一个问题：这些方法在分类单元缺失的随机模型中是否在统计上保持一致，并断言它们这样做。在这里，我们提供了这些定理之一的反例，并确定在I.I.D.下，Astrid和NJST在统计学上不一致。分类单元删除的模型。
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.
