Consistency of the maximum likelihood estimator of population tree in a coalescent framework

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Journal of Statistical Planning and Inference Pub Date : 2024-04-21 DOI:10.1016/j.jspi.2024.106172
Arindam RoyChoudhury
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Abstract

We present a proof of consistency of the maximum likelihood estimator (MLE) of population tree in a previously proposed coalescent model. As the model involves tree-topology as a parameter, the standard proof of consistency for continuous parameters does not directly apply. In addition to proving that a consistent sequence of MLE exists, we also prove that the overall MLE, computed by maximizing the likelihood over all tree-topologies, is also consistent. Thus, the MLE of tree-topology is consistent as well. The last result is important because local maxima occur in the likelihood of population trees, especially while maximizing the likelihood separately for each tree-topology. Even though MLE is known to be a dependable estimator under this model, our work proves its effectiveness with mathematical certainty.

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聚合框架中种群树最大似然估计的一致性
我们提出了种群树最大似然估计值(MLE)在之前提出的聚合模型中的一致性证明。由于该模型涉及作为参数的树顶结构,因此连续参数的标准一致性证明并不直接适用。除了证明存在一致的 MLE 序列外,我们还证明了通过最大化所有树状结构的似然计算得出的整体 MLE 也是一致的。因此,树状结构的 MLE 也是一致的。最后一个结果非常重要,因为种群树的可能性会出现局部最大值,尤其是在对每种树形分别进行可能性最大化时。尽管众所周知 MLE 是该模型下可靠的估计器,但我们的工作还是用数学上的确定性证明了它的有效性。
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来源期刊
Journal of Statistical Planning and Inference
Journal of Statistical Planning and Inference 数学-统计学与概率论
CiteScore
2.10
自引率
11.10%
发文量
78
审稿时长
3-6 weeks
期刊介绍: The Journal of Statistical Planning and Inference offers itself as a multifaceted and all-inclusive bridge between classical aspects of statistics and probability, and the emerging interdisciplinary aspects that have a potential of revolutionizing the subject. While we maintain our traditional strength in statistical inference, design, classical probability, and large sample methods, we also have a far more inclusive and broadened scope to keep up with the new problems that confront us as statisticians, mathematicians, and scientists. We publish high quality articles in all branches of statistics, probability, discrete mathematics, machine learning, and bioinformatics. We also especially welcome well written and up to date review articles on fundamental themes of statistics, probability, machine learning, and general biostatistics. Thoughtful letters to the editors, interesting problems in need of a solution, and short notes carrying an element of elegance or beauty are equally welcome.
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