Combining information across diverse sources: The II-CC-FF paradigm

We introduce and develop a general paradigm for combining information across diverse data sources. In broad terms, suppose phi is a parameter of interest, built up via components psi 1, horizontal ellipsis ,psi k from data sources 1, horizontal ellipsis , 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) Cj(psi j) for the relevant focus parameter 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, horizontal ellipsis ,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.

Automated summary

The proposed method introduces a general paradigm for combining information from diverse data sources through three steps: independent inspection, confidence conversion, and focused fusion. This method, equivalent to versions of meta-analysis in traditional setups, demonstrates competitiveness when applied to more difficult problems.