We introduce and develop a general paradigm for combining information across diverse data sources. In broad terms, suppose $\phi$$\phi$ is a parameter of interest, built up via components ${\psi }_1,\dots ,{\psi }_k$${\psi }_1,\dots ,{\psi }_k$ from data sources 1, … , k. The proposed scheme has three steps. First, the independent inspection (II) step amounts to investigating each separate data source, translating statistical information to a confidence distribution (CD) $C_j({\psi }_j)$$C_j({\psi }_j)$ for the relevant focus parameter ${\psi }_j$${\psi }_j$ associated with data source j. Second, confidence conversion (CC) techniques are used to translate the CDs to confidence log-likelihood functions. Finally, the focused fusion (FF) step uses relevant and context-driven techniques to construct a confidence distribution for the primary focus parameter $\phi =\phi ({\psi }_1,\dots ,{\psi }_k)$$\phi =\phi ({\psi }_1,\dots ,{\psi }_k)$, acting on the combined confidence log-likelihood. In traditional setups, the II-CC-FF strategy amounts to versions of meta-analysis, and turns out to be competitive against state-of-the-art methods. Its potential lies in applications to harder problems, however. Illustrations are presented, related to actual applications.

中文翻译：

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结合不同来源的信息：II-CC-FF 范式

我们引入并开发了一种通用范式，用于跨不同数据源组合信息。广义上，假设$\phi$$\phi$是一个感兴趣的参数，通过组件建立${\psi }_1,\dots ,{\psi }_{ķ}$${\psi }_1,\dots ,{\psi }_k$来自数据源 1, … ,  k。提议的方案分为三个步骤。首先，独立检查 (II) 步骤相当于调查每个单独的数据源，将统计信息转换为置信度分布 (CD)$C_j({\psi }_j)$$C_j({\psi }_j)$对于相关的焦点参数${\psi }_j$${\psi }_j$与数据源j相关联。其次，置信度转换 (CC) 技术用于将 CD 转换为置信度对数似然函数。最后，聚焦融合 (FF) 步骤使用相关和上下文驱动的技术来构建主要聚焦参数的置信度分布$\phi =\phi ({\psi }_1,\dots ,{\psi }_{ķ})$$\phi =\phi ({\psi }_1,\dots ,{\psi }_k)$，作用于组合置信度对数似然。在传统设置中，II-CC-FF 策略相当于元分析的版本，结果证明与最先进的方法具有竞争力。然而，它的潜力在于应用于更难的问题。插图与实际应用相关。