The integrative analysis of multiple datasets is an important strategy in data analysis. It is increasingly popular in genomics, which enjoys a wealth of publicly available datasets that can be compared, contrasted, and combined in order to extract novel scientific insights. This paper studies a stylized example of data integration for a classical statistical problem: leveraging side information to estimate a vector of normal means. This task is formulated as a compound decision problem, an oracle integrative decision rule is derived, and a data-driven estimate of this rule based on minimizing an unbiased estimate of its risk is proposed. The data-driven rule is shown to asymptotically achieve the minimum possible risk among all separable decision rules, and it can outperform existing methods in numerical properties. The proposed procedure leads naturally to an integrative high-dimensional classification procedure, which is illustrated by combining data from two independent gene expression profiling studies.

中文翻译：

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具有边信息的正态均值的同时估计

多数据集的综合分析是数据分析中的一个重要策略。它在基因组学中越来越受欢迎，基因组学拥有大量公开可用的数据集，这些数据集可以进行比较、对比和组合，以提取新的科学见解。本文研究了经典统计问题的数据集成的典型示例：利用边信息来估计正态均值的向量。该任务被表述为一个复合决策问题，推导出一个预言机综合决策规则，并基于最小化其风险的无偏估计提出了对该规则的数据驱动估计。数据驱动规则被证明在所有可分离的决策规则中渐近地实现最小可能的风险，并且它可以在数值属性上优于现有方法。