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首页> 外文期刊>Journal of Mathematical Biology >Consistency of estimators of population scaled parameters using composite likelihood
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Consistency of estimators of population scaled parameters using composite likelihood

机译：使用复合似然法的人口规模参数估计量的一致性

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Composite likelihood methods have become very popular for the analysis of large-scale genomic data sets because of the computational intractability of the basic coalescent process and its generalizations: It is virtually impossible to calculate the likelihood of an observed data set spanning a large chromosomal region without using approximate or heuristic methods. Composite likelihood methods are approximate methods and, in the present article, assume the likelihood is written as a product of likelihoods, one for each of a number of smaller regions that together make up the whole region from which data is collected. A very general framework for neutral coalescent models is presented and discussed. The framework comprises many of the most popular coalescent models that are currently used for analysis of genetic data. Assume data is collected from a series of consecutive regions of equal size. Then it is shown that the observed data forms a stationary, ergodic process. General conditions are given under which the maximum composite estimator of the parameters describing the model (e.g. mutation rates, demographic parameters and the recombination rate) is a consistent estimator as the number of regions tends to infinity.
机译：由于基本合并过程的计算难点及其概括性，复合似然方法已成为分析大型基因组数据集的一种非常普遍的方法：实际上，如果没有观测到的数据集跨越大染色体区域的可能性，则几乎不可能计算出这种可能性使用近似或启发式方法。复合似然法是一种近似方法，在本文中，假定似然被写为似然的乘积，一个较小的区域中的每个区域都构成一个可能性，这些区域共同构成了从中收集数据的整个区域。提出并讨论了中性合并模型的非常通用的框架。该框架包含许多当前用于遗传数据分析的最受欢迎的合并模型。假设从一系列大小相等的连续区域收集数据。然后表明，观察到的数据形成了平稳的遍历过程。给出了一般条件，在该条件下，随着区域数量趋于无穷大，描述模型的参数的最大复合估计量（例如突变率，人口统计学参数和重组率）是一致的估计量。

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