通过频率比估计分集
Estimating Diversity via Frequency Ratios
摘要：
我们希望根据样本计数来估计种群中的类的总数，特别是在存在高潜在多样性的情况下。利用概率论的特点，分布的整数连续概率的比率，我们构建了一个非线性回归模型的连续频率计数的比率。这使我们能够预测未观察到的计数，从而估计总的多样性。我们认为，这是第一个办法，离开经典的混合泊松模型在这个问题。我们的方法是几何直观的，并产生良好的拟合数据与合理的标准误。它特别适合分析来自微生物生态学中下一代测序的高多样性数据集。我们证明了该方法的性能在这种情况下，并通过模拟，我们提出了一个数据集，我们的方法优于所有竞争对手。
英文摘要：
We wish to estimate the total number of classes in a population based on sample counts, especially in the presence of high latent diversity. Drawing on probability theory that characterizes distributions on the integers by ratios of consecutive probabilities, we construct a nonlinear regression model for the ratios of consecutive frequency counts. This allows us to predict the unobserved count and hence estimate the total diversity. We believe that this is the first approach to depart from the classical mixed Poisson model in this problem. Our method is geometrically intuitive and yields good fits to data with reasonable standard errors. It is especially well-suited to analyzing high diversity datasets derived from next-generation sequencing in microbial ecology. We demonstrate the method's performance in this context and via simulation, and we present a dataset for which our method outperforms all competitors.
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