成分数据中基于扰动的效应度量
Perturbation-based Effect Measures for Compositional Data
AI总结：
针对成分数据现有效应度量不适用于高维稀疏场景、无偏评估成分统计量对响应变量影响困难的问题，提出基于假设数据扰动的平均扰动效应框架，通过重参数化和半参数技术高效估计，经多数据集验证优于现有方法。
摘要：
现有的成分特征效应度量方法不适用于诸多现代应用场景，例如微生物组研究，因为这类数据具有高维度、稀疏性等特性，传统参数方法难以对其建模。此外，以无偏方式评估成分的汇总统计量（如种族多样性）如何影响响应变量并非易事。我们提出一个基于假设数据扰动的框架，在成分本身之上定义了可解释的统计泛函，我们称之为平均扰动效应。这些效应能自然地校正会使常用边际依赖分析产生偏差的混杂因素。我们通过推导依赖于扰动的重参数化方法并应用半参数估计技术，展示了如何高效估计平均扰动效应。我们在模拟数据和半合成数据上对所提出的估计量进行了实证分析，并在纽约学校数据和微生物组数据上证明了该方法优于现有技术。
英文摘要：
Existing effect measures for compositional features are inadequate for many modern applications, for example, in microbiome research, since they display traits such as high-dimensionality and sparsity that can be poorly modelled with traditional parametric approaches. Further, assessing -- in an unbiased way -- how summary statistics of a composition (e.g., racial diversity) affect a response variable is not straightforward. We propose a framework based on hypothetical data perturbations which defines interpretable statistical functionals on the compositions themselves, which we call average perturbation effects. These effects naturally account for confounding that biases frequently used marginal dependence analyses. We show how average perturbation effects can be estimated efficiently by deriving a perturbation-dependent reparametrization and applying semiparametric estimation techniques. We analyze the proposed estimators empirically on simulated and semi-synthetic data and demonstrate advantages over existing techniques on data from New York schools and microbiome data.
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