Combining Information Across Diverse Sources: The II-CC-FF Paradigm

TL;DR

This paper presents a new general paradigm for combining information from multiple diverse data sources using confidence distributions, with a three-step process that improves meta-analysis and can be applied to complex problems.

Contribution

The paper introduces the II-CC-FF paradigm, a novel three-step method for integrating data sources via confidence distributions, enhancing traditional meta-analysis techniques.

Findings

Competitive with state-of-the-art methods in traditional setups

Effective in applications involving complex data integration

Potential for tackling harder, real-world problems

Abstract

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 $\psi_1,\ldots,\psi_k$ from data sources $1,\ldots,k$. 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 $C_j(\psi_j)$ for the relevant focus parameter $\psi_j$ associated with data source $j$. Second, Confidence Conversion (CC) techniques are used to translate the confidence distributions to confidence log-likelihood functions, say $\ell_{{\rm con},j}(\psi_j)$. Finally, the Focused Fusion (FF) step uses relevant and context-driven techniques to construct a confidence distribution for the primary focus parameter $\phi=\phi(\psi_1,\ldots,\psi_k)$, 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.