带有选择的$\mathbf{k}$ -等位基因模型下的最大似然估计在数值上可能是不稳定的
Maximum likelihood estimates under $\mathbf{k}$-allele models with selection can be numerically unstable
摘要：
在具有选择和亲本独立突变的各种Wright-Fisher $k$ -等位基因模型下，等位基因频率的平稳分布得到了很好的研究。然而，这些模型下的参数的最大似然估计的统计特性还没有得到很好的理解。在这些模型中的每一个模型下，数据空间中都有一个点携带最强的选择信号，然而，在这一点上，可能性是无限的。即使假设所有的突变参数都是已知的，这个结果仍然有效。因此，标准的模拟方法用于近似的抽样分布的最大似然估计产生数值上不稳定的结果，在大量的选择。我们描述了贝叶斯替代的后验分布往往会产生更准确和可靠的区间估计的选择强度在一个位点。
英文摘要：
The stationary distribution of allele frequencies under a variety of Wright--Fisher $k$-allele models with selection and parent independent mutation is well studied. However, the statistical properties of maximum likelihood estimates of parameters under these models are not well understood. Under each of these models there is a point in data space which carries the strongest possible signal for selection, yet, at this point, the likelihood is unbounded. This result remains valid even if all of the mutation parameters are assumed to be known. Therefore, standard simulation approaches used to approximate the sampling distribution of the maximum likelihood estimate produce numerically unstable results in the presence of substantial selection. We describe the Bayesian alternative where the posterior distribution tends to produce more accurate and reliable interval estimates for the selection intensity at a locus.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
